Validation
Kara Tsang, Kat Holt
Last compiled on 06/05/25
load data
MBIRA_Kpn <- read_delim("MBIRA_ValidationOutput/Prediction_Pheno_summary_Kpn.csv")
MBIRA_Kvv <- read_delim("MBIRA_ValidationOutput/Prediction_Pheno_summary_Kvv.csv")
HowdenGorrie <- read_delim("HowdenGorrie_ValidationOutput/Prediction_Pheno_summary.csv")
AGAR <- read_delim("AGAR_ValidationOutput/Prediction_Pheno_summary.csv")
Milan <- read_delim("MilanSanRafaelle_ValidationOutput/Prediction_Pheno_summary.csv")
HailuEthiopia <- read_delim("HailuEthiopia_ValidationOutput/Prediction_Pheno_summary.csv")
FeaseyMalawi <- read_delim("FeaseyMalawi_ValidationOutput/Prediction_Pheno_summary.csv")
KayamaJapan <- read_delim("KayamaJapan_ValidationOutput/Prediction_Pheno_summary.csv")
EUSCAPE_EURECA <- read_delim("EURECA_EUSCAPE_ValidationOutput/Prediction_Pheno_summary.csv")
EgliSwitzerland <- read_delim("EgliSwitzerland_2025_ValidationOutput/Prediction_Pheno_summary.csv")
HRYC_Spain <- read_delim("HRYC_Spain_ValidationOutput/Prediction_Pheno_summary.csv")
IndiaGHRU <- read_delim("IndiaGHRU_2025_ValidationOutput/Prediction_Pheno_summary.csv")
NeumannGermany <- read_delim("NeumannGermany_ValidationOutput/Prediction_Pheno_summary.csv")
Whitelaw_CHAMPS <- read_delim("Whitelaw_Stellenbosch_ValidationOutput/Prediction_Pheno_summary_DD.csv")
Oxfordshire_Kpn <- read_delim("Oxfordshire_ValidationOutput/Prediction_Pheno_summary_Kpn.csv")
Oxfordshire_Kvv <- read_delim("Oxfordshire_ValidationOutput/Prediction_Pheno_summary_Kvv.csv")
IZSAM_Italy <- read_delim("IZSAM_Italy_ValidationOutput/Prediction_Pheno_summary.csv")
LaPaz_Lazaro <- read_delim("LaPaz_Lazaro_ValidationOutput/Prediction_Pheno_summary.csv")
HarrisMERINO <- read_delim("HarrisMERINO_ValidationOutput/Prediction_Pheno_summary.csv")
PachecoBrazil <- read_delim("PachecoBrazil_ValidationOutput/Prediction_Pheno_summary.csv")set colours
wt_colours <- c(`non-wt (I/R)`="IndianRed", `wt (S)`= "LightBlue")
res_colours <- c("I"="grey", "R"="IndianRed", "S"="LightBlue", "NWT"="grey")
res_colours2 <- c("I"="black", "R"="IndianRed", "S"="LightBlue", "NWT"="black")
res_colours3 <- c("NWT I"="#78638a", "NWT R"="IndianRed", "WT S"="LightBlue")functions to calculate PPV and categorical agreement from models
cat_agree <- function(xtabs) {
return((xtabs[1,1]+xtabs[2,2])/sum(xtabs))
}
cat_agree_lb <- function(xtabs) {
return(
((xtabs[1,1]+xtabs[2,2])/sum(xtabs)) - (1.96 * sqrt(((xtabs[1,1]+xtabs[2,2])/sum(xtabs)) * ((1 - ((xtabs[1,1]+xtabs[2,2])/sum(xtabs)))) / sum(xtabs)))
)
}
cat_agree_ub <- function(xtabs) {
return(
((xtabs[1,1]+xtabs[2,2])/sum(xtabs)) + (1.96 * sqrt(((xtabs[1,1]+xtabs[2,2])/sum(xtabs)) * ((1 - ((xtabs[1,1]+xtabs[2,2])/sum(xtabs)))) / sum(xtabs)))
)
}
ppv <- function(xtabs) {
return(xtabs[2,2]/sum(xtabs[2,]))
}
ppv_lb <- function(xtabs) {
return(
xtabs[2,2]/(sum(xtabs[2,])) - (1.96 * sqrt((xtabs[2,2]/(sum(xtabs[2,]))) * ((1 - xtabs[2,2]/(sum(xtabs[2,]))) / sum(xtabs[2,])))
))
}
ppv_ub <- function(xtabs) {
return(
xtabs[2,2]/(sum(xtabs[2,])) + (1.96 * sqrt((xtabs[2,2]/(sum(xtabs[2,]))) * ((1 - xtabs[2,2]/(sum(xtabs[2,]))) / sum(xtabs[2,])))
))
}
npv <- function(xtabs) {
return(xtabs[1,1]/sum(xtabs[1,]))
}
npv_lb <- function(xtabs) {
return(
xtabs[1,1]/(sum(xtabs[1,])) - (1.96 * sqrt((xtabs[1,1]/(sum(xtabs[1,]))) * ((1 - xtabs[1,1]/(sum(xtabs[1,]))) / sum(xtabs[1,])))
))
}
npv_ub <- function(xtabs) {
return(
xtabs[1,1]/(sum(xtabs[1,])) + (1.96 * sqrt((xtabs[1,1]/(sum(xtabs[1,]))) * ((1 - xtabs[1,1]/(sum(xtabs[1,]))) / sum(xtabs[1,])))
))
}
#sens <- function(pred,truth) {
# xtabs <- table(pred,truth)
# return(xtabs[2,2]/(sum(xtabs[,2])))
#}
sens <- function(xtabs) {
return(xtabs[2,2]/(sum(xtabs[,2])))
}
sens_lb <- function(xtabs) {
return(
xtabs[2,2]/(sum(xtabs[,2])) - (1.96 * sqrt((xtabs[2,2]/(sum(xtabs[,2]))) * ((1 - xtabs[2,2]/(sum(xtabs[,2]))) / sum(xtabs[,2])))
))
}
sens_ub <- function(xtabs) {
return(
xtabs[2,2]/(sum(xtabs[,2])) + (1.96 * sqrt((xtabs[2,2]/(sum(xtabs[,2]))) * ((1 - xtabs[2,2]/(sum(xtabs[,2]))) / sum(xtabs[,2])))
))
}
spec <- function(xtabs) {
return(xtabs[1,1]/(sum(xtabs[,1])))
}
spec_lb <- function(xtabs) {
return(
xtabs[1,1]/(sum(xtabs[,1])) - (1.96 * sqrt((xtabs[1,1]/(sum(xtabs[,1]))) * ((1 - xtabs[1,1]/(sum(xtabs[,1]))) / sum(xtabs[,1])))
))
}
spec_ub <- function(xtabs) {
return(
xtabs[1,1]/(sum(xtabs[,1])) + (1.96 * sqrt((xtabs[1,1]/(sum(xtabs[,1]))) * ((1 - xtabs[1,1]/(sum(xtabs[,1]))) / sum(xtabs[,1])))
))
}
me <- function(xtabs) {
return(xtabs[2,1]/sum(xtabs[,1]))
}
me_lb <- function(xtabs) {
return(
xtabs[2,1]/(sum(xtabs[,1])) - (1.96 * sqrt((xtabs[2,1]/(sum(xtabs[,1]))) * ((1 - xtabs[2,1]/(sum(xtabs[,1]))) / sum(xtabs[,1])))
))
}
me_ub <- function(xtabs) {
return(
xtabs[2,1]/(sum(xtabs[,1])) + (1.96 * sqrt((xtabs[2,1]/(sum(xtabs[,1]))) * ((1 - xtabs[2,1]/(sum(xtabs[,1]))) / sum(xtabs[,1])))
))
}
vme <- function(xtabs) {
return(xtabs[1,2]/sum(xtabs[,2]))
}
vme_lb <- function(xtabs) {
return(
xtabs[1,2]/(sum(xtabs[,2])) - (1.96 * sqrt((xtabs[1,2]/(sum(xtabs[,2]))) * ((1 - xtabs[1,2]/(sum(xtabs[,2]))) / sum(xtabs[,2])))
))
}
vme_ub <- function(xtabs) {
return(
xtabs[1,2]/(sum(xtabs[,2])) + (1.96 * sqrt((xtabs[1,2]/(sum(xtabs[,2]))) * ((1 - xtabs[1,2]/(sum(xtabs[,2]))) / sum(xtabs[,2])))
))
}
perc_R <- function(xtabs) {
return(sum(xtabs[,2])/sum(xtabs))
}
metrics <- function(xtabs) {
return(list(cat=cat_agree(xtabs),
cat_lb=cat_agree_lb(xtabs),
cat_ub=cat_agree_ub(xtabs),
ppv=ppv(xtabs),
ppv_lb=ppv_lb(xtabs),
ppv_ub=ppv_ub(xtabs),
npv=npv(xtabs),
npv_lb=npv_lb(xtabs),
npv_ub=npv_ub(xtabs),
sens=sens(xtabs),
sens_lb=sens_lb(xtabs),
sens_ub=sens_ub(xtabs),
spec=spec(xtabs),
spec_lb=spec_lb(xtabs),
spec_ub=spec_ub(xtabs),
me=me(xtabs),
me_lb=me_lb(xtabs),
me_ub=me_ub(xtabs),
vme=vme(xtabs),
vme_lb=vme_lb(xtabs),
vme_ub=vme_ub(xtabs),
perc_R=perc_R(xtabs),
n=sum(xtabs),
summary=c("cat"=cat_agree(xtabs),
"sens"=sens(xtabs),
"spec"=spec(xtabs),
"me"=me(xtabs),
"vme"=vme(xtabs),
"cat_lb"=cat_agree_lb(xtabs),
"cat_ub"=cat_agree_ub(xtabs),
"sens_lb"=sens_lb(xtabs),
"sens_ub"=sens_ub(xtabs),
"spec_lb"=spec_lb(xtabs),
"spec_ub"=spec_ub(xtabs),
"me_lb"=me_lb(xtabs),
"me_ub"=me_ub(xtabs),
"vme_lb"=vme_lb(xtabs),
"vme_ub"=vme_ub(xtabs),
"perc_R"=perc_R(xtabs),
"n"=sum(xtabs)
)
)
)
}data wrangling
MBIRA_Kpn_SI <- MBIRA_Kpn %>%
rename(Group=...1) %>%
filter(Group=="pred_S" | Group=="pred_I") %>%
rowwise()%>%
mutate(S_I = sum(S, I)) %>%
mutate(I_R = sum(I, R)) %>%
select(-c(I)) %>%
adorn_totals("row") %>%
mutate(Group = gsub("Total", "pred_S_I", Group))
MBIRA_Kpn_R <- MBIRA_Kpn %>%
rename(Group=...1) %>%
filter(Group=="pred_R" | Group=="pred_I") %>%
rowwise()%>%
mutate(S_I = sum(S, I)) %>%
mutate(I_R = sum(I, R)) %>%
select(-c(I)) %>%
adorn_totals("row") %>%
mutate(Group = gsub("Total", "pred_I_R", Group))
MBIRA_Kpn_SIR <- rbind(MBIRA_Kpn_SI, MBIRA_Kpn_R) %>% distinct()
subset(MBIRA_Kpn_SIR, Group == "pred_S_I")$S_I## [1] 69##
## 12 57 58 69 70
## 1 1 1 1 1MBIRA_Kpn_R_prediction <- matrix(c(subset(MBIRA_Kpn_SIR, Group == "pred_S_I")$S_I,
subset(MBIRA_Kpn_SIR, Group == "pred_S_I")$R,
subset(MBIRA_Kpn_SIR, Group == "pred_R")$S_I,
subset(MBIRA_Kpn_SIR, Group == "pred_R")$R), ncol=2, byrow=TRUE)
colnames(MBIRA_Kpn_R_prediction) <- c('S_I','R')
rownames(MBIRA_Kpn_R_prediction) <- c('pred_S_I','pred_R')
MBIRA_Kpn_R_prediction_summary<-metrics(MBIRA_Kpn_R_prediction)
MBIRA_Kpn_NWT_prediction <- matrix(c(subset(MBIRA_Kpn_SIR, Group == "pred_S")$S,
subset(MBIRA_Kpn_SIR, Group == "pred_S")$I_R,
subset(MBIRA_Kpn_SIR, Group == "pred_I_R")$S,
subset(MBIRA_Kpn_SIR, Group == "pred_I_R")$I_R), ncol=2, byrow=TRUE)
colnames(MBIRA_Kpn_NWT_prediction) <- c('S','I_R')
rownames(MBIRA_Kpn_NWT_prediction) <- c('pred_S','pred_I_R')
MBIRA_Kpn_NWT_prediction_summary<-metrics(MBIRA_Kpn_NWT_prediction)MBIRA_Kvv_SI <- MBIRA_Kvv %>%
rename(Group=...1) %>%
filter(Group=="pred_S" | Group=="pred_I") %>%
rowwise()%>%
mutate(S_I = sum(S, I)) %>%
mutate(I_R = sum(I, R)) %>%
select(-c(I)) %>%
adorn_totals("row") %>%
mutate(Group = gsub("Total", "pred_S_I", Group))
MBIRA_Kvv_R <- MBIRA_Kvv %>%
rename(Group=...1) %>%
filter(Group=="pred_R" | Group=="pred_I") %>%
rowwise()%>%
mutate(S_I = sum(S, I)) %>%
mutate(I_R = sum(I, R)) %>%
select(-c(I)) %>%
adorn_totals("row") %>%
mutate(Group = gsub("Total", "pred_I_R", Group))
MBIRA_Kvv_SIR <- rbind(MBIRA_Kvv_SI, MBIRA_Kvv_R) %>% distinct()
subset(MBIRA_Kvv_SIR, Group == "pred_S_I")$S_I## [1] 86##
## 0 86
## 3 2MBIRA_Kvv_R_prediction <- matrix(c(subset(MBIRA_Kvv_SIR, Group == "pred_S_I")$S_I,
subset(MBIRA_Kvv_SIR, Group == "pred_S_I")$R,
subset(MBIRA_Kvv_SIR, Group == "pred_R")$S_I,
subset(MBIRA_Kvv_SIR, Group == "pred_R")$R), ncol=2, byrow=TRUE)
colnames(MBIRA_Kvv_R_prediction) <- c('S_I','R')
rownames(MBIRA_Kvv_R_prediction) <- c('pred_S_I','pred_R')
MBIRA_Kvv_R_prediction_summary<-metrics(MBIRA_Kvv_R_prediction)
MBIRA_Kvv_NWT_prediction <- matrix(c(subset(MBIRA_Kvv_SIR, Group == "pred_S")$S,
subset(MBIRA_Kvv_SIR, Group == "pred_S")$I_R,
subset(MBIRA_Kvv_SIR, Group == "pred_I_R")$S,
subset(MBIRA_Kvv_SIR, Group == "pred_I_R")$I_R), ncol=2, byrow=TRUE)
colnames(MBIRA_Kvv_NWT_prediction) <- c('S','I_R')
rownames(MBIRA_Kvv_NWT_prediction) <- c('pred_S','pred_I_R')
MBIRA_Kvv_NWT_prediction_summary<-metrics(MBIRA_Kvv_NWT_prediction)HowdenGorrie_SI <- HowdenGorrie %>%
rename(Group=...1) %>%
filter(Group=="pred_S" | Group=="pred_I") %>%
rowwise()%>%
mutate(S_I = sum(S, I)) %>%
mutate(I_R = sum(I, R)) %>%
select(-c(I)) %>%
adorn_totals("row") %>%
mutate(Group = gsub("Total", "pred_S_I", Group))
HowdenGorrie_R <- HowdenGorrie %>%
rename(Group=...1) %>%
filter(Group=="pred_R" | Group=="pred_I") %>%
rowwise()%>%
mutate(S_I = sum(S, I)) %>%
mutate(I_R = sum(I, R)) %>%
select(-c(I)) %>%
adorn_totals("row") %>%
mutate(Group = gsub("Total", "pred_I_R", Group))
HowdenGorrie_SIR <- rbind(HowdenGorrie_SI, HowdenGorrie_R) %>% distinct()
subset(HowdenGorrie_SIR, Group == "pred_S_I")$S_I## [1] 44##
## 3 15 18 41 44
## 1 1 1 1 1HowdenGorrie_R_prediction <- matrix(c(subset(HowdenGorrie_SIR, Group == "pred_S_I")$S_I,
subset(HowdenGorrie_SIR, Group == "pred_S_I")$R,
subset(HowdenGorrie_SIR, Group == "pred_R")$S_I,
subset(HowdenGorrie_SIR, Group == "pred_R")$R), ncol=2, byrow=TRUE)
colnames(HowdenGorrie_R_prediction) <- c('S_I','R')
rownames(HowdenGorrie_R_prediction) <- c('pred_S_I','pred_R')
HowdenGorrie_R_prediction_summary<-metrics(HowdenGorrie_R_prediction)
HowdenGorrie_NWT_prediction <- matrix(c(subset(HowdenGorrie_SIR, Group == "pred_S")$S,
subset(HowdenGorrie_SIR, Group == "pred_S")$I_R,
subset(HowdenGorrie_SIR, Group == "pred_I_R")$S,
subset(HowdenGorrie_SIR, Group == "pred_I_R")$I_R), ncol=2, byrow=TRUE)
colnames(HowdenGorrie_NWT_prediction) <- c('S','I_R')
rownames(HowdenGorrie_NWT_prediction) <- c('pred_S','pred_I_R')
HowdenGorrie_NWT_prediction_summary<-metrics(HowdenGorrie_NWT_prediction)AGAR_SI <- AGAR %>%
rename(Group=...1) %>%
filter(Group=="pred_S" | Group=="pred_I") %>%
rowwise()%>%
mutate(S_I = sum(S, I)) %>%
mutate(I_R = sum(I, R)) %>%
select(-c(I)) %>%
adorn_totals("row") %>%
mutate(Group = gsub("Total", "pred_S_I", Group))
AGAR_R <- AGAR %>%
rename(Group=...1) %>%
filter(Group=="pred_R" | Group=="pred_I") %>%
rowwise()%>%
mutate(S_I = sum(S, I)) %>%
mutate(I_R = sum(I, R)) %>%
select(-c(I)) %>%
adorn_totals("row") %>%
mutate(Group = gsub("Total", "pred_I_R", Group))
AGAR_SIR <- rbind(AGAR_SI, AGAR_R) %>% distinct()
subset(AGAR_SIR, Group == "pred_S_I")$S_I## [1] 178##
## 19 52 71 159 178
## 1 1 1 1 1AGAR_R_prediction <- matrix(c(subset(AGAR_SIR, Group == "pred_S_I")$S_I,
subset(AGAR_SIR, Group == "pred_S_I")$R,
subset(AGAR_SIR, Group == "pred_R")$S_I,
subset(AGAR_SIR, Group == "pred_R")$R), ncol=2, byrow=TRUE)
colnames(AGAR_R_prediction) <- c('S_I','R')
rownames(AGAR_R_prediction) <- c('pred_S_I','pred_R')
AGAR_R_prediction_summary<-metrics(AGAR_R_prediction)
AGAR_NWT_prediction <- matrix(c(subset(AGAR_SIR, Group == "pred_S")$S,
subset(AGAR_SIR, Group == "pred_S")$I_R,
subset(AGAR_SIR, Group == "pred_I_R")$S,
subset(AGAR_SIR, Group == "pred_I_R")$I_R), ncol=2, byrow=TRUE)
colnames(AGAR_NWT_prediction) <- c('S','I_R')
rownames(AGAR_NWT_prediction) <- c('pred_S','pred_I_R')
AGAR_NWT_prediction_summary<-metrics(AGAR_NWT_prediction)Milan_SI <- Milan %>%
rename(Group=...1) %>%
filter(Group=="pred_S" | Group=="pred_I") %>%
rowwise()%>%
mutate(S_I = sum(S, I)) %>%
mutate(I_R = sum(I, R)) %>%
select(-c(I)) %>%
adorn_totals("row") %>%
mutate(Group = gsub("Total", "pred_S_I", Group))
Milan_R <- Milan %>%
rename(Group=...1) %>%
filter(Group=="pred_R" | Group=="pred_I") %>%
rowwise()%>%
mutate(S_I = sum(S, I)) %>%
mutate(I_R = sum(I, R)) %>%
select(-c(I)) %>%
adorn_totals("row") %>%
mutate(Group = gsub("Total", "pred_I_R", Group))
Milan_SIR <- rbind(Milan_SI, Milan_R) %>% distinct()
subset(Milan_SIR, Group == "pred_S_I")$S_I## [1] 9##
## 0 2 9
## 1 2 2Milan_R_prediction <- matrix(c(subset(Milan_SIR, Group == "pred_S_I")$S_I,
subset(Milan_SIR, Group == "pred_S_I")$R,
subset(Milan_SIR, Group == "pred_R")$S_I,
subset(Milan_SIR, Group == "pred_R")$R), ncol=2, byrow=TRUE)
colnames(Milan_R_prediction) <- c('S_I','R')
rownames(Milan_R_prediction) <- c('pred_S_I','pred_R')
Milan_R_prediction_summary<-metrics(Milan_R_prediction)
Milan_NWT_prediction <- matrix(c(subset(Milan_SIR, Group == "pred_S")$S,
subset(Milan_SIR, Group == "pred_S")$I_R,
subset(Milan_SIR, Group == "pred_I_R")$S,
subset(Milan_SIR, Group == "pred_I_R")$I_R), ncol=2, byrow=TRUE)
colnames(Milan_NWT_prediction) <- c('S','I_R')
rownames(Milan_NWT_prediction) <- c('pred_S','pred_I_R')
Milan_NWT_prediction_summary<-metrics(Milan_NWT_prediction)HailuEthiopia_SI <- HailuEthiopia %>%
rename(Group=...1) %>%
filter(Group=="pred_S" | Group=="pred_I") %>%
rowwise()%>%
mutate(S_I = sum(S, I)) %>%
mutate(I_R = sum(I, R)) %>%
select(-c(I)) %>%
adorn_totals("row") %>%
mutate(Group = gsub("Total", "pred_S_I", Group))
HailuEthiopia_R <- HailuEthiopia %>%
rename(Group=...1) %>%
filter(Group=="pred_R" | Group=="pred_I") %>%
rowwise()%>%
mutate(S_I = sum(S, I)) %>%
mutate(I_R = sum(I, R)) %>%
select(-c(I)) %>%
adorn_totals("row") %>%
mutate(Group = gsub("Total", "pred_I_R", Group))
HailuEthiopia_SIR <- rbind(HailuEthiopia_SI, HailuEthiopia_R) %>% distinct()
subset(HailuEthiopia_SIR, Group == "pred_S_I")$S_I## [1] 42##
## 0 42
## 3 2HailuEthiopia_R_prediction <- matrix(c(subset(HailuEthiopia_SIR, Group == "pred_S_I")$S_I,
subset(HailuEthiopia_SIR, Group == "pred_S_I")$R,
subset(HailuEthiopia_SIR, Group == "pred_R")$S_I,
subset(HailuEthiopia_SIR, Group == "pred_R")$R), ncol=2, byrow=TRUE)
colnames(HailuEthiopia_R_prediction) <- c('S_I','R')
rownames(HailuEthiopia_R_prediction) <- c('pred_S_I','pred_R')
HailuEthiopia_R_prediction_summary<-metrics(HailuEthiopia_R_prediction)
HailuEthiopia_NWT_prediction <- matrix(c(subset(HailuEthiopia_SIR, Group == "pred_S")$S,
subset(HailuEthiopia_SIR, Group == "pred_S")$I_R,
subset(HailuEthiopia_SIR, Group == "pred_I_R")$S,
subset(HailuEthiopia_SIR, Group == "pred_I_R")$I_R), ncol=2, byrow=TRUE)
colnames(HailuEthiopia_NWT_prediction) <- c('S','I_R')
rownames(HailuEthiopia_NWT_prediction) <- c('pred_S','pred_I_R')
HailuEthiopia_NWT_prediction_summary<-metrics(HailuEthiopia_NWT_prediction)KayamaJapan_SI <- KayamaJapan %>%
rename(Group=...1) %>%
filter(Group=="pred_S" | Group=="pred_I") %>%
rowwise()%>%
mutate(S_I = sum(S, I)) %>%
mutate(I_R = sum(I, R)) %>%
select(-c(I)) %>%
adorn_totals("row") %>%
mutate(Group = gsub("Total", "pred_S_I", Group))
KayamaJapan_R <- KayamaJapan %>%
rename(Group=...1) %>%
filter(Group=="pred_R" | Group=="pred_I") %>%
rowwise()%>%
mutate(S_I = sum(S, I)) %>%
mutate(I_R = sum(I, R)) %>%
select(-c(I)) %>%
adorn_totals("row") %>%
mutate(Group = gsub("Total", "pred_I_R", Group))
KayamaJapan_SIR <- rbind(KayamaJapan_SI, KayamaJapan_R) %>% distinct()
subset(KayamaJapan_SIR, Group == "pred_S_I")$S_I## [1] 229##
## 17 156 173 212 229
## 1 1 1 1 1KayamaJapan_R_prediction <- matrix(c(subset(KayamaJapan_SIR, Group == "pred_S_I")$S_I,
subset(KayamaJapan_SIR, Group == "pred_S_I")$R,
subset(KayamaJapan_SIR, Group == "pred_R")$S_I,
subset(KayamaJapan_SIR, Group == "pred_R")$R), ncol=2, byrow=TRUE)
colnames(KayamaJapan_R_prediction) <- c('S_I','R')
rownames(KayamaJapan_R_prediction) <- c('pred_S_I','pred_R')
KayamaJapan_R_prediction_summary<-metrics(KayamaJapan_R_prediction)
KayamaJapan_NWT_prediction <- matrix(c(subset(KayamaJapan_SIR, Group == "pred_S")$S,
subset(KayamaJapan_SIR, Group == "pred_S")$I_R,
subset(KayamaJapan_SIR, Group == "pred_I_R")$S,
subset(KayamaJapan_SIR, Group == "pred_I_R")$I_R), ncol=2, byrow=TRUE)
colnames(KayamaJapan_NWT_prediction) <- c('S','I_R')
rownames(KayamaJapan_NWT_prediction) <- c('pred_S','pred_I_R')
KayamaJapan_NWT_prediction_summary<-metrics(KayamaJapan_NWT_prediction)FeaseyMalawi_SI <- FeaseyMalawi %>%
rename(Group=...1) %>%
filter(Group=="pred_S" | Group=="pred_I") %>%
rowwise()%>%
mutate(S_I = sum(S, I)) %>%
mutate(I_R = sum(I, R)) %>%
select(-c(I)) %>%
adorn_totals("row") %>%
mutate(Group = gsub("Total", "pred_S_I", Group))
FeaseyMalawi_R <- FeaseyMalawi %>%
rename(Group=...1) %>%
filter(Group=="pred_R" | Group=="pred_I") %>%
rowwise()%>%
mutate(S_I = sum(S, I)) %>%
mutate(I_R = sum(I, R)) %>%
select(-c(I)) %>%
adorn_totals("row") %>%
mutate(Group = gsub("Total", "pred_I_R", Group))
FeaseyMalawi_SIR <- rbind(FeaseyMalawi_SI, FeaseyMalawi_R) %>% distinct()
subset(FeaseyMalawi_SIR, Group == "pred_S_I")$S_I## [1] 311##
## 5 15 20 306 311
## 1 1 1 1 1FeaseyMalawi_R_prediction <- matrix(c(subset(FeaseyMalawi_SIR, Group == "pred_S_I")$S_I,
subset(FeaseyMalawi_SIR, Group == "pred_S_I")$R,
subset(FeaseyMalawi_SIR, Group == "pred_R")$S_I,
subset(FeaseyMalawi_SIR, Group == "pred_R")$R), ncol=2, byrow=TRUE)
colnames(FeaseyMalawi_R_prediction) <- c('S_I','R')
rownames(FeaseyMalawi_R_prediction) <- c('pred_S_I','pred_R')
FeaseyMalawi_R_prediction_summary<-metrics(FeaseyMalawi_R_prediction)
FeaseyMalawi_NWT_prediction <- matrix(c(subset(FeaseyMalawi_SIR, Group == "pred_S")$S,
subset(FeaseyMalawi_SIR, Group == "pred_S")$I_R,
subset(FeaseyMalawi_SIR, Group == "pred_I_R")$S,
subset(FeaseyMalawi_SIR, Group == "pred_I_R")$I_R), ncol=2, byrow=TRUE)
colnames(FeaseyMalawi_NWT_prediction) <- c('S','I_R')
rownames(FeaseyMalawi_NWT_prediction) <- c('pred_S','pred_I_R')
FeaseyMalawi_NWT_prediction_summary<-metrics(FeaseyMalawi_NWT_prediction)EUSCAPE_EURECA_SI <- EUSCAPE_EURECA %>%
rename(Group=...1) %>%
filter(Group=="pred_S" | Group=="pred_I") %>%
rowwise()%>%
mutate(S_I = sum(S, I)) %>%
mutate(I_R = sum(I, R)) %>%
select(-c(I)) %>%
adorn_totals("row") %>%
mutate(Group = gsub("Total", "pred_S_I", Group))
EUSCAPE_EURECA_R <- EUSCAPE_EURECA %>%
rename(Group=...1) %>%
filter(Group=="pred_R" | Group=="pred_I") %>%
rowwise()%>%
mutate(S_I = sum(S, I)) %>%
mutate(I_R = sum(I, R)) %>%
select(-c(I)) %>%
adorn_totals("row") %>%
mutate(Group = gsub("Total", "pred_I_R", Group))
EUSCAPE_EURECA_SIR <- rbind(EUSCAPE_EURECA_SI, EUSCAPE_EURECA_R) %>% distinct()
subset(EUSCAPE_EURECA_SIR, Group == "pred_S_I")$S_I## [1] 430##
## 2 16 18 428 430
## 1 1 1 1 1EUSCAPE_EURECA_R_prediction <- matrix(c(subset(EUSCAPE_EURECA_SIR, Group == "pred_S_I")$S_I,
subset(EUSCAPE_EURECA_SIR, Group == "pred_S_I")$R,
subset(EUSCAPE_EURECA_SIR, Group == "pred_R")$S_I,
subset(EUSCAPE_EURECA_SIR, Group == "pred_R")$R), ncol=2, byrow=TRUE)
colnames(EUSCAPE_EURECA_R_prediction) <- c('S_I','R')
rownames(EUSCAPE_EURECA_R_prediction) <- c('pred_S_I','pred_R')
EUSCAPE_EURECA_R_prediction_summary<-metrics(EUSCAPE_EURECA_R_prediction)
EUSCAPE_EURECA_NWT_prediction <- matrix(c(subset(EUSCAPE_EURECA_SIR, Group == "pred_S")$S,
subset(EUSCAPE_EURECA_SIR, Group == "pred_S")$I_R,
subset(EUSCAPE_EURECA_SIR, Group == "pred_I_R")$S,
subset(EUSCAPE_EURECA_SIR, Group == "pred_I_R")$I_R), ncol=2, byrow=TRUE)
colnames(EUSCAPE_EURECA_NWT_prediction) <- c('S','I_R')
rownames(EUSCAPE_EURECA_NWT_prediction) <- c('pred_S','pred_I_R')
EUSCAPE_EURECA_NWT_prediction_summary<-metrics(EUSCAPE_EURECA_NWT_prediction)EgliSwitzerland_SI <- EgliSwitzerland %>%
rename(Group=...1) %>%
filter(Group=="pred_S" | Group=="pred_I") %>%
rowwise()%>%
mutate(S_I = sum(S, I)) %>%
mutate(I_R = sum(I, R)) %>%
select(-c(I)) %>%
adorn_totals("row") %>%
mutate(Group = gsub("Total", "pred_S_I", Group))
EgliSwitzerland_R <- EgliSwitzerland %>%
rename(Group=...1) %>%
filter(Group=="pred_R" | Group=="pred_I") %>%
rowwise()%>%
mutate(S_I = sum(S, I)) %>%
mutate(I_R = sum(I, R)) %>%
select(-c(I)) %>%
adorn_totals("row") %>%
mutate(Group = gsub("Total", "pred_I_R", Group))
EgliSwitzerland_SIR <- rbind(EgliSwitzerland_SI, EgliSwitzerland_R) %>% distinct()
subset(EgliSwitzerland_SIR, Group == "pred_S_I")$S_I## [1] 13##
## 1 6 7 12 13
## 1 1 1 1 1EgliSwitzerland_R_prediction <- matrix(c(subset(EgliSwitzerland_SIR, Group == "pred_S_I")$S_I,
subset(EgliSwitzerland_SIR, Group == "pred_S_I")$R,
subset(EgliSwitzerland_SIR, Group == "pred_R")$S_I,
subset(EgliSwitzerland_SIR, Group == "pred_R")$R), ncol=2, byrow=TRUE)
colnames(EgliSwitzerland_R_prediction) <- c('S_I','R')
rownames(EgliSwitzerland_R_prediction) <- c('pred_S_I','pred_R')
EgliSwitzerland_R_prediction_summary<-metrics(EgliSwitzerland_R_prediction)
EgliSwitzerland_NWT_prediction <- matrix(c(subset(EgliSwitzerland_SIR, Group == "pred_S")$S,
subset(EgliSwitzerland_SIR, Group == "pred_S")$I_R,
subset(EgliSwitzerland_SIR, Group == "pred_I_R")$S,
subset(EgliSwitzerland_SIR, Group == "pred_I_R")$I_R), ncol=2, byrow=TRUE)
colnames(EgliSwitzerland_NWT_prediction) <- c('S','I_R')
rownames(EgliSwitzerland_NWT_prediction) <- c('pred_S','pred_I_R')
EgliSwitzerland_NWT_prediction_summary<-metrics(EgliSwitzerland_NWT_prediction)HRYC_Spain_SI <- HRYC_Spain %>%
rename(Group=...1) %>%
filter(Group=="pred_S" | Group=="pred_I") %>%
rowwise()%>%
mutate(S_I = sum(S, I)) %>%
mutate(I_R = sum(I, R)) %>%
select(-c(I)) %>%
adorn_totals("row") %>%
mutate(Group = gsub("Total", "pred_S_I", Group))
HRYC_Spain_R <- HRYC_Spain %>%
rename(Group=...1) %>%
filter(Group=="pred_R" | Group=="pred_I") %>%
rowwise()%>%
mutate(S_I = sum(S, I)) %>%
mutate(I_R = sum(I, R)) %>%
select(-c(I)) %>%
adorn_totals("row") %>%
mutate(Group = gsub("Total", "pred_I_R", Group))
HRYC_Spain_SIR <- rbind(HRYC_Spain_SI, HRYC_Spain_R) %>% distinct()
subset(HRYC_Spain_SIR, Group == "pred_S_I")$S_I## [1] 11##
## 0 11 16
## 1 2 2HRYC_Spain_R_prediction <- matrix(c(subset(HRYC_Spain_SIR, Group == "pred_S_I")$S_I,
subset(HRYC_Spain_SIR, Group == "pred_S_I")$R,
subset(HRYC_Spain_SIR, Group == "pred_R")$S_I,
subset(HRYC_Spain_SIR, Group == "pred_R")$R), ncol=2, byrow=TRUE)
colnames(HRYC_Spain_R_prediction) <- c('S_I','R')
rownames(HRYC_Spain_R_prediction) <- c('pred_S_I','pred_R')
HRYC_Spain_R_prediction_summary<-metrics(HRYC_Spain_R_prediction)
HRYC_Spain_NWT_prediction <- matrix(c(subset(HRYC_Spain_SIR, Group == "pred_S")$S,
subset(HRYC_Spain_SIR, Group == "pred_S")$I_R,
subset(HRYC_Spain_SIR, Group == "pred_I_R")$S,
subset(HRYC_Spain_SIR, Group == "pred_I_R")$I_R), ncol=2, byrow=TRUE)
colnames(HRYC_Spain_NWT_prediction) <- c('S','I_R')
rownames(HRYC_Spain_NWT_prediction) <- c('pred_S','pred_I_R')
HRYC_Spain_NWT_prediction_summary<-metrics(HRYC_Spain_NWT_prediction)IndiaGHRU_SI <- IndiaGHRU %>%
rename(Group=...1) %>%
filter(Group=="pred_S" | Group=="pred_I") %>%
rowwise()%>%
mutate(S_I = sum(S, I)) %>%
mutate(I_R = sum(I, R)) %>%
select(-c(I)) %>%
adorn_totals("row") %>%
mutate(Group = gsub("Total", "pred_S_I", Group))
IndiaGHRU_R <- IndiaGHRU %>%
rename(Group=...1) %>%
filter(Group=="pred_R" | Group=="pred_I") %>%
rowwise()%>%
mutate(S_I = sum(S, I)) %>%
mutate(I_R = sum(I, R)) %>%
select(-c(I)) %>%
adorn_totals("row") %>%
mutate(Group = gsub("Total", "pred_I_R", Group))
IndiaGHRU_SIR <- rbind(IndiaGHRU_SI, IndiaGHRU_R) %>% distinct()
subset(IndiaGHRU_SIR, Group == "pred_S_I")$S_I## [1] 70##
## 2 25 27 68 70
## 1 1 1 1 1IndiaGHRU_R_prediction <- matrix(c(subset(IndiaGHRU_SIR, Group == "pred_S_I")$S_I,
subset(IndiaGHRU_SIR, Group == "pred_S_I")$R,
subset(IndiaGHRU_SIR, Group == "pred_R")$S_I,
subset(IndiaGHRU_SIR, Group == "pred_R")$R), ncol=2, byrow=TRUE)
colnames(IndiaGHRU_R_prediction) <- c('S_I','R')
rownames(IndiaGHRU_R_prediction) <- c('pred_S_I','pred_R')
IndiaGHRU_R_prediction_summary<-metrics(IndiaGHRU_R_prediction)
IndiaGHRU_NWT_prediction <- matrix(c(subset(IndiaGHRU_SIR, Group == "pred_S")$S,
subset(IndiaGHRU_SIR, Group == "pred_S")$I_R,
subset(IndiaGHRU_SIR, Group == "pred_I_R")$S,
subset(IndiaGHRU_SIR, Group == "pred_I_R")$I_R), ncol=2, byrow=TRUE)
colnames(IndiaGHRU_NWT_prediction) <- c('S','I_R')
rownames(IndiaGHRU_NWT_prediction) <- c('pred_S','pred_I_R')
IndiaGHRU_NWT_prediction_summary<-metrics(IndiaGHRU_NWT_prediction)NeumannGermany_SI <- NeumannGermany %>%
rename(Group=...1) %>%
filter(Group=="pred_S" | Group=="pred_I") %>%
rowwise()%>%
mutate(S_I = sum(S, I)) %>%
mutate(I_R = sum(I, R)) %>%
select(-c(I)) %>%
adorn_totals("row") %>%
mutate(Group = gsub("Total", "pred_S_I", Group))
NeumannGermany_R <- NeumannGermany %>%
rename(Group=...1) %>%
filter(Group=="pred_R" | Group=="pred_I") %>%
rowwise()%>%
mutate(S_I = sum(S, I)) %>%
mutate(I_R = sum(I, R)) %>%
select(-c(I)) %>%
adorn_totals("row") %>%
mutate(Group = gsub("Total", "pred_I_R", Group))
NeumannGermany_SIR <- rbind(NeumannGermany_SI, NeumannGermany_R) %>% distinct()
subset(NeumannGermany_SIR, Group == "pred_S_I")$S_I## [1] 51##
## 0 3 51
## 1 2 2NeumannGermany_R_prediction <- matrix(c(subset(NeumannGermany_SIR, Group == "pred_S_I")$S_I,
subset(NeumannGermany_SIR, Group == "pred_S_I")$R,
subset(NeumannGermany_SIR, Group == "pred_R")$S_I,
subset(NeumannGermany_SIR, Group == "pred_R")$R), ncol=2, byrow=TRUE)
colnames(NeumannGermany_R_prediction) <- c('S_I','R')
rownames(NeumannGermany_R_prediction) <- c('pred_S_I','pred_R')
NeumannGermany_R_prediction_summary<-metrics(NeumannGermany_R_prediction)
NeumannGermany_NWT_prediction <- matrix(c(subset(NeumannGermany_SIR, Group == "pred_S")$S,
subset(NeumannGermany_SIR, Group == "pred_S")$I_R,
subset(NeumannGermany_SIR, Group == "pred_I_R")$S,
subset(NeumannGermany_SIR, Group == "pred_I_R")$I_R), ncol=2, byrow=TRUE)
colnames(NeumannGermany_NWT_prediction) <- c('S','I_R')
rownames(NeumannGermany_NWT_prediction) <- c('pred_S','pred_I_R')
NeumannGermany_NWT_prediction_summary<-metrics(NeumannGermany_NWT_prediction)Whitelaw_CHAMPS_SI <- Whitelaw_CHAMPS %>%
rename(Group=...1) %>%
filter(Group=="pred_S" | Group=="pred_I") %>%
rowwise()%>%
mutate(S_I = sum(S, I)) %>%
mutate(I_R = sum(I, R)) %>%
select(-c(I)) %>%
adorn_totals("row") %>%
mutate(Group = gsub("Total", "pred_S_I", Group))
Whitelaw_CHAMPS_R <- Whitelaw_CHAMPS %>%
rename(Group=...1) %>%
filter(Group=="pred_R" | Group=="pred_I") %>%
rowwise()%>%
mutate(S_I = sum(S, I)) %>%
mutate(I_R = sum(I, R)) %>%
select(-c(I)) %>%
adorn_totals("row") %>%
mutate(Group = gsub("Total", "pred_I_R", Group))
Whitelaw_CHAMPS_SIR <- rbind(Whitelaw_CHAMPS_SI, Whitelaw_CHAMPS_R) %>% distinct()
subset(Whitelaw_CHAMPS_SIR, Group == "pred_S_I")$S_I## [1] 18##
## 0 1 18
## 1 2 2Whitelaw_CHAMPS_R_prediction <- matrix(c(subset(Whitelaw_CHAMPS_SIR, Group == "pred_S_I")$S_I,
subset(Whitelaw_CHAMPS_SIR, Group == "pred_S_I")$R,
subset(Whitelaw_CHAMPS_SIR, Group == "pred_R")$S_I,
subset(Whitelaw_CHAMPS_SIR, Group == "pred_R")$R), ncol=2, byrow=TRUE)
colnames(Whitelaw_CHAMPS_R_prediction) <- c('S_I','R')
rownames(Whitelaw_CHAMPS_R_prediction) <- c('pred_S_I','pred_R')
Whitelaw_CHAMPS_R_prediction_summary<-metrics(Whitelaw_CHAMPS_R_prediction)
Whitelaw_CHAMPS_NWT_prediction <- matrix(c(subset(Whitelaw_CHAMPS_SIR, Group == "pred_S")$S,
subset(Whitelaw_CHAMPS_SIR, Group == "pred_S")$I_R,
subset(Whitelaw_CHAMPS_SIR, Group == "pred_I_R")$S,
subset(Whitelaw_CHAMPS_SIR, Group == "pred_I_R")$I_R), ncol=2, byrow=TRUE)
colnames(Whitelaw_CHAMPS_NWT_prediction) <- c('S','I_R')
rownames(Whitelaw_CHAMPS_NWT_prediction) <- c('pred_S','pred_I_R')
Whitelaw_CHAMPS_NWT_prediction_summary<-metrics(Whitelaw_CHAMPS_NWT_prediction)Oxfordshire_Kpn_SI <- Oxfordshire_Kpn %>%
rename(Group=...1) %>%
filter(Group=="pred_S" | Group=="pred_I") %>%
rowwise()%>%
mutate(S_I = sum(S, I)) %>%
mutate(I_R = sum(I, R)) %>%
select(-c(I)) %>%
adorn_totals("row") %>%
mutate(Group = gsub("Total", "pred_S_I", Group))
Oxfordshire_Kpn_R <- Oxfordshire_Kpn %>%
rename(Group=...1) %>%
filter(Group=="pred_R" | Group=="pred_I") %>%
rowwise()%>%
mutate(S_I = sum(S, I)) %>%
mutate(I_R = sum(I, R)) %>%
select(-c(I)) %>%
adorn_totals("row") %>%
mutate(Group = gsub("Total", "pred_I_R", Group))
Oxfordshire_Kpn_SIR <- rbind(Oxfordshire_Kpn_SI, Oxfordshire_Kpn_R) %>% distinct()
subset(Oxfordshire_Kpn_SIR, Group == "pred_S_I")$S_I## [1] 252##
## 6 12 246 252
## 2 1 1 1Oxfordshire_Kpn_R_prediction <- matrix(c(subset(Oxfordshire_Kpn_SIR, Group == "pred_S_I")$S_I,
subset(Oxfordshire_Kpn_SIR, Group == "pred_S_I")$R,
subset(Oxfordshire_Kpn_SIR, Group == "pred_R")$S_I,
subset(Oxfordshire_Kpn_SIR, Group == "pred_R")$R), ncol=2, byrow=TRUE)
colnames(Oxfordshire_Kpn_R_prediction) <- c('S_I','R')
rownames(Oxfordshire_Kpn_R_prediction) <- c('pred_S_I','pred_R')
Oxfordshire_Kpn_R_prediction_summary<-metrics(Oxfordshire_Kpn_R_prediction)
Oxfordshire_Kpn_NWT_prediction <- matrix(c(subset(Oxfordshire_Kpn_SIR, Group == "pred_S")$S,
subset(Oxfordshire_Kpn_SIR, Group == "pred_S")$I_R,
subset(Oxfordshire_Kpn_SIR, Group == "pred_I_R")$S,
subset(Oxfordshire_Kpn_SIR, Group == "pred_I_R")$I_R), ncol=2, byrow=TRUE)
colnames(Oxfordshire_Kpn_NWT_prediction) <- c('S','I_R')
rownames(Oxfordshire_Kpn_NWT_prediction) <- c('pred_S','pred_I_R')
Oxfordshire_Kpn_NWT_prediction_summary<-metrics(Oxfordshire_Kpn_NWT_prediction)Oxfordshire_Kvv_SI <- Oxfordshire_Kvv %>%
rename(Group=...1) %>%
filter(Group=="pred_S" | Group=="pred_I") %>%
rowwise()%>%
mutate(S_I = sum(S, I)) %>%
mutate(I_R = sum(I, R)) %>%
select(-c(I)) %>%
adorn_totals("row") %>%
mutate(Group = gsub("Total", "pred_S_I", Group))
Oxfordshire_Kvv_R <- Oxfordshire_Kvv %>%
rename(Group=...1) %>%
filter(Group=="pred_R" | Group=="pred_I") %>%
rowwise()%>%
mutate(S_I = sum(S, I)) %>%
mutate(I_R = sum(I, R)) %>%
select(-c(I)) %>%
adorn_totals("row") %>%
mutate(Group = gsub("Total", "pred_I_R", Group))
Oxfordshire_Kvv_SIR <- rbind(Oxfordshire_Kvv_SI, Oxfordshire_Kvv_R) %>% distinct()
subset(Oxfordshire_Kvv_SIR, Group == "pred_S_I")$S_I## [1] 65##
## 0 1 65
## 1 2 2Oxfordshire_Kvv_R_prediction <- matrix(c(subset(Oxfordshire_Kvv_SIR, Group == "pred_S_I")$S_I,
subset(Oxfordshire_Kvv_SIR, Group == "pred_S_I")$R,
subset(Oxfordshire_Kvv_SIR, Group == "pred_R")$S_I,
subset(Oxfordshire_Kvv_SIR, Group == "pred_R")$R), ncol=2, byrow=TRUE)
colnames(Oxfordshire_Kvv_R_prediction) <- c('S_I','R')
rownames(Oxfordshire_Kvv_R_prediction) <- c('pred_S_I','pred_R')
Oxfordshire_Kvv_R_prediction_summary<-metrics(Oxfordshire_Kvv_R_prediction)
Oxfordshire_Kvv_NWT_prediction <- matrix(c(subset(Oxfordshire_Kvv_SIR, Group == "pred_S")$S,
subset(Oxfordshire_Kvv_SIR, Group == "pred_S")$I_R,
subset(Oxfordshire_Kvv_SIR, Group == "pred_I_R")$S,
subset(Oxfordshire_Kvv_SIR, Group == "pred_I_R")$I_R), ncol=2, byrow=TRUE)
colnames(Oxfordshire_Kvv_NWT_prediction) <- c('S','I_R')
rownames(Oxfordshire_Kvv_NWT_prediction) <- c('pred_S','pred_I_R')
Oxfordshire_Kvv_NWT_prediction_summary<-metrics(Oxfordshire_Kvv_NWT_prediction)IZSAM_Italy_SI <- IZSAM_Italy %>%
rename(Group=...1) %>%
filter(Group=="pred_S" | Group=="pred_I") %>%
rowwise()%>%
mutate(S_I = sum(S, I)) %>%
mutate(I_R = sum(I, R)) %>%
select(-c(I)) %>%
adorn_totals("row") %>%
mutate(Group = gsub("Total", "pred_S_I", Group))
IZSAM_Italy_R <- IZSAM_Italy %>%
rename(Group=...1) %>%
filter(Group=="pred_R" | Group=="pred_I") %>%
rowwise()%>%
mutate(S_I = sum(S, I)) %>%
mutate(I_R = sum(I, R)) %>%
select(-c(I)) %>%
adorn_totals("row") %>%
mutate(Group = gsub("Total", "pred_I_R", Group))
IZSAM_Italy_SIR <- rbind(IZSAM_Italy_SI, IZSAM_Italy_R) %>% distinct()
subset(IZSAM_Italy_SIR, Group == "pred_S_I")$S_I## [1] 7##
## 0 3 7
## 1 2 2IZSAM_Italy_R_prediction <- matrix(c(subset(IZSAM_Italy_SIR, Group == "pred_S_I")$S_I,
subset(IZSAM_Italy_SIR, Group == "pred_S_I")$R,
subset(IZSAM_Italy_SIR, Group == "pred_R")$S_I,
subset(IZSAM_Italy_SIR, Group == "pred_R")$R), ncol=2, byrow=TRUE)
colnames(IZSAM_Italy_R_prediction) <- c('S_I','R')
rownames(IZSAM_Italy_R_prediction) <- c('pred_S_I','pred_R')
IZSAM_Italy_R_prediction_summary<-metrics(IZSAM_Italy_R_prediction)
IZSAM_Italy_NWT_prediction <- matrix(c(subset(IZSAM_Italy_SIR, Group == "pred_S")$S,
subset(IZSAM_Italy_SIR, Group == "pred_S")$I_R,
subset(IZSAM_Italy_SIR, Group == "pred_I_R")$S,
subset(IZSAM_Italy_SIR, Group == "pred_I_R")$I_R), ncol=2, byrow=TRUE)
colnames(IZSAM_Italy_NWT_prediction) <- c('S','I_R')
rownames(IZSAM_Italy_NWT_prediction) <- c('pred_S','pred_I_R')
IZSAM_Italy_NWT_prediction_summary<-metrics(IZSAM_Italy_NWT_prediction)LaPaz_Lazaro_SI <- LaPaz_Lazaro %>%
rename(Group=...1) %>%
filter(Group=="pred_S" | Group=="pred_I") %>%
rowwise()%>%
mutate(S_I = sum(S, I)) %>%
mutate(I_R = sum(I, R)) %>%
select(-c(I)) %>%
adorn_totals("row") %>%
mutate(Group = gsub("Total", "pred_S_I", Group))
LaPaz_Lazaro_R <- LaPaz_Lazaro %>%
rename(Group=...1) %>%
filter(Group=="pred_R" | Group=="pred_I") %>%
rowwise()%>%
mutate(S_I = sum(S, I)) %>%
mutate(I_R = sum(I, R)) %>%
select(-c(I)) %>%
adorn_totals("row") %>%
mutate(Group = gsub("Total", "pred_I_R", Group))
LaPaz_Lazaro_SIR <- rbind(LaPaz_Lazaro_SI, LaPaz_Lazaro_R) %>% distinct()
subset(LaPaz_Lazaro_SIR, Group == "pred_S_I")$S_I## [1] 11##
## 0 1 11
## 1 2 2LaPaz_Lazaro_R_prediction <- matrix(c(subset(LaPaz_Lazaro_SIR, Group == "pred_S_I")$S_I,
subset(LaPaz_Lazaro_SIR, Group == "pred_S_I")$R,
subset(LaPaz_Lazaro_SIR, Group == "pred_R")$S_I,
subset(LaPaz_Lazaro_SIR, Group == "pred_R")$R), ncol=2, byrow=TRUE)
colnames(LaPaz_Lazaro_R_prediction) <- c('S_I','R')
rownames(LaPaz_Lazaro_R_prediction) <- c('pred_S_I','pred_R')
LaPaz_Lazaro_R_prediction_summary<-metrics(LaPaz_Lazaro_R_prediction)
LaPaz_Lazaro_NWT_prediction <- matrix(c(subset(LaPaz_Lazaro_SIR, Group == "pred_S")$S,
subset(LaPaz_Lazaro_SIR, Group == "pred_S")$I_R,
subset(LaPaz_Lazaro_SIR, Group == "pred_I_R")$S,
subset(LaPaz_Lazaro_SIR, Group == "pred_I_R")$I_R), ncol=2, byrow=TRUE)
colnames(LaPaz_Lazaro_NWT_prediction) <- c('S','I_R')
rownames(LaPaz_Lazaro_NWT_prediction) <- c('pred_S','pred_I_R')
LaPaz_Lazaro_NWT_prediction_summary<-metrics(LaPaz_Lazaro_NWT_prediction)HarrisMERINO_SI <- HarrisMERINO %>%
rename(Group=...1) %>%
filter(Group=="pred_S" | Group=="pred_I") %>%
rowwise()%>%
mutate(S_I = sum(S, I)) %>%
mutate(I_R = sum(I, R)) %>%
select(-c(I)) %>%
adorn_totals("row") %>%
mutate(Group = gsub("Total", "pred_S_I", Group))
HarrisMERINO_R <- HarrisMERINO %>%
rename(Group=...1) %>%
filter(Group=="pred_R" | Group=="pred_I") %>%
rowwise()%>%
mutate(S_I = sum(S, I)) %>%
mutate(I_R = sum(I, R)) %>%
select(-c(I)) %>%
adorn_totals("row") %>%
mutate(Group = gsub("Total", "pred_I_R", Group))
HarrisMERINO_SIR <- rbind(HarrisMERINO_SI, HarrisMERINO_R) %>% distinct()
subset(HarrisMERINO_SIR, Group == "pred_S_I")$S_I## [1] 13##
## 2 5 7 8 13
## 1 1 1 1 1HarrisMERINO_R_prediction <- matrix(c(subset(HarrisMERINO_SIR, Group == "pred_S_I")$S_I,
subset(HarrisMERINO_SIR, Group == "pred_S_I")$R,
subset(HarrisMERINO_SIR, Group == "pred_R")$S_I,
subset(HarrisMERINO_SIR, Group == "pred_R")$R), ncol=2, byrow=TRUE)
colnames(HarrisMERINO_R_prediction) <- c('S_I','R')
rownames(HarrisMERINO_R_prediction) <- c('pred_S_I','pred_R')
HarrisMERINO_R_prediction_summary<-metrics(HarrisMERINO_R_prediction)
HarrisMERINO_NWT_prediction <- matrix(c(subset(HarrisMERINO_SIR, Group == "pred_S")$S,
subset(HarrisMERINO_SIR, Group == "pred_S")$I_R,
subset(HarrisMERINO_SIR, Group == "pred_I_R")$S,
subset(HarrisMERINO_SIR, Group == "pred_I_R")$I_R), ncol=2, byrow=TRUE)
colnames(HarrisMERINO_NWT_prediction) <- c('S','I_R')
rownames(HarrisMERINO_NWT_prediction) <- c('pred_S','pred_I_R')
HarrisMERINO_NWT_prediction_summary<-metrics(HarrisMERINO_NWT_prediction)PachecoBrazil_SI <- PachecoBrazil %>%
rename(Group=...1) %>%
filter(Group=="pred_S" | Group=="pred_I") %>%
rowwise()%>%
mutate(S_I = sum(S, I)) %>%
mutate(I_R = sum(I, R)) %>%
select(-c(I)) %>%
adorn_totals("row") %>%
mutate(Group = gsub("Total", "pred_S_I", Group))
PachecoBrazil_R <- PachecoBrazil %>%
rename(Group=...1) %>%
filter(Group=="pred_R" | Group=="pred_I") %>%
rowwise()%>%
mutate(S_I = sum(S, I)) %>%
mutate(I_R = sum(I, R)) %>%
select(-c(I)) %>%
adorn_totals("row") %>%
mutate(Group = gsub("Total", "pred_I_R", Group))
PachecoBrazil_SIR <- rbind(PachecoBrazil_SI, PachecoBrazil_R) %>% distinct()
subset(PachecoBrazil_SIR, Group == "pred_S_I")$S_I## [1] 10##
## 0 10
## 3 2PachecoBrazil_R_prediction <- matrix(c(subset(PachecoBrazil_SIR, Group == "pred_S_I")$S_I,
subset(PachecoBrazil_SIR, Group == "pred_S_I")$R,
subset(PachecoBrazil_SIR, Group == "pred_R")$S_I,
subset(PachecoBrazil_SIR, Group == "pred_R")$R), ncol=2, byrow=TRUE)
colnames(PachecoBrazil_R_prediction) <- c('S_I','R')
rownames(PachecoBrazil_R_prediction) <- c('pred_S_I','pred_R')
PachecoBrazil_R_prediction_summary<-metrics(PachecoBrazil_R_prediction)
PachecoBrazil_NWT_prediction <- matrix(c(subset(PachecoBrazil_SIR, Group == "pred_S")$S,
subset(PachecoBrazil_SIR, Group == "pred_S")$I_R,
subset(PachecoBrazil_SIR, Group == "pred_I_R")$S,
subset(PachecoBrazil_SIR, Group == "pred_I_R")$I_R), ncol=2, byrow=TRUE)
colnames(PachecoBrazil_NWT_prediction) <- c('S','I_R')
rownames(PachecoBrazil_NWT_prediction) <- c('pred_S','pred_I_R')
PachecoBrazil_NWT_prediction_summary<-metrics(PachecoBrazil_NWT_prediction)pooled_R <-
bind_rows(MBIRA_Kpn_SIR,
MBIRA_Kvv_SIR,
HowdenGorrie_SIR,
AGAR_SIR,
Milan_SIR,
HailuEthiopia_SIR,
KayamaJapan_SIR,
FeaseyMalawi_SIR,
EUSCAPE_EURECA_SIR,
EgliSwitzerland_SIR,
HRYC_Spain_SIR,
IndiaGHRU_SIR,
NeumannGermany_SIR,
Whitelaw_CHAMPS_SIR,
Oxfordshire_Kpn_SIR,
Oxfordshire_Kvv_SIR,
IZSAM_Italy_SIR,
LaPaz_Lazaro_SIR,
HarrisMERINO_SIR,
PachecoBrazil_SIR) %>%
group_by(Group) %>%
summarise_all(funs(sum(., na.rm = TRUE)))
pooled_R_prediction <- matrix(c(subset(pooled_R, Group == "pred_S_I")$S_I,
subset(pooled_R, Group == "pred_S_I")$R,
subset(pooled_R, Group == "pred_R")$S_I,
subset(pooled_R, Group == "pred_R")$R), ncol=2, byrow=TRUE)
colnames(pooled_R_prediction) <- c('S_I','R')
rownames(pooled_R_prediction) <- c('pred_S_I','pred_R')
pooled_R_summary<-metrics(pooled_R_prediction)summary of pooled genotypes vs. SIR
EUSCAPE_EURECA_genotype <- EUSCAPE_EURECA %>%
rename(Group=...1) %>%
filter(Group!="pred_S" & Group!="pred_I"& Group!="pred_R")
KayamaJapan_genotype <- KayamaJapan %>%
rename(Group=...1) %>%
filter(Group!="pred_S" & Group!="pred_I"& Group!="pred_R")
IndiaGHRU_genotype <- IndiaGHRU %>%
rename(Group=...1) %>%
filter(Group!="pred_S" & Group!="pred_I"& Group!="pred_R")
AGAR_genotype <- AGAR %>%
rename(Group=...1) %>%
filter(Group!="pred_S" & Group!="pred_I"& Group!="pred_R")
Oxfordshire_Kpn_genotype <-Oxfordshire_Kpn %>%
rename(Group=...1) %>%
filter(Group!="pred_S" & Group!="pred_I"& Group!="pred_R")
Oxfordshire_Kvv_genotype <-Oxfordshire_Kpn %>%
rename(Group=...1) %>%
filter(Group!="pred_S" & Group!="pred_I"& Group!="pred_R")
MBIRA_Kpn_genotype <- MBIRA_Kpn %>%
rename(Group=...1) %>%
filter(Group!="pred_S" & Group!="pred_I"& Group!="pred_R")
HowdenGorrie_genotype <- HowdenGorrie %>%
rename(Group=...1) %>%
filter(Group!="pred_S" & Group!="pred_I"& Group!="pred_R")
Milan_genotype <- Milan %>%
rename(Group=...1) %>%
filter(Group!="pred_S" & Group!="pred_I"& Group!="pred_R")
HRYC_Spain_genotype <- HRYC_Spain %>%
rename(Group=...1) %>%
filter(Group!="pred_S" & Group!="pred_I"& Group!="pred_R")
LaPaz_Lazaro_genotype <- LaPaz_Lazaro %>%
rename(Group=...1) %>%
filter(Group!="pred_S" & Group!="pred_I"& Group!="pred_R")
MBIRA_Kvv_genotype <- MBIRA_Kvv %>%
rename(Group=...1) %>%
filter(Group!="pred_S" & Group!="pred_I"& Group!="pred_R")
HarrisMERINO_genotype <- HarrisMERINO %>%
rename(Group=...1) %>%
filter(Group!="pred_S" & Group!="pred_I"& Group!="pred_R")
PachecoBrazil_genotype <- PachecoBrazil %>%
rename(Group=...1) %>%
filter(Group!="pred_S" & Group!="pred_I"& Group!="pred_R")
IZSAM_Italy_genotype <- IZSAM_Italy %>%
rename(Group=...1) %>%
filter(Group!="pred_S" & Group!="pred_I"& Group!="pred_R")
HailuEthiopia_genotype <- HailuEthiopia %>%
rename(Group=...1) %>%
filter(Group!="pred_S" & Group!="pred_I"& Group!="pred_R")
FeaseyMalawi_genotype <- FeaseyMalawi %>%
rename(Group=...1) %>%
filter(Group!="pred_S" & Group!="pred_I"& Group!="pred_R")
EgliSwitzerland_genotype <- EgliSwitzerland %>%
rename(Group=...1) %>%
filter(Group!="pred_S" & Group!="pred_I"& Group!="pred_R")
NeumannGermany_genotype <- NeumannGermany %>%
rename(Group=...1) %>%
filter(Group!="pred_S" & Group!="pred_I"& Group!="pred_R")
Whitelaw_CHAMPS_genotype <- Whitelaw_CHAMPS %>%
rename(Group=...1) %>%
filter(Group!="pred_S" & Group!="pred_I"& Group!="pred_R")
pooled_genotype <-
bind_rows(MBIRA_Kpn_genotype,
MBIRA_Kvv_genotype,
HowdenGorrie_genotype,
AGAR_genotype,
Milan_genotype,
HailuEthiopia_genotype,
KayamaJapan_genotype,
FeaseyMalawi_genotype,
EUSCAPE_EURECA_genotype,
EgliSwitzerland_genotype,
HRYC_Spain_genotype,
IndiaGHRU_genotype,
NeumannGermany_genotype,
Whitelaw_CHAMPS_genotype,
Oxfordshire_Kpn_genotype,
Oxfordshire_Kvv_genotype,
IZSAM_Italy_genotype,
LaPaz_Lazaro_genotype,
HarrisMERINO_genotype,
PachecoBrazil_genotype) %>%
group_by(Group) %>%
summarise_all(funs(sum(., na.rm = TRUE)))pooled_NWT <-
bind_rows(MBIRA_Kpn_SIR,
MBIRA_Kvv_SIR,
HowdenGorrie_SIR,
AGAR_SIR,
Milan_SIR,
HailuEthiopia_SIR,
KayamaJapan_SIR,
FeaseyMalawi_SIR,
EUSCAPE_EURECA_SIR,
EgliSwitzerland_SIR,
HRYC_Spain_SIR,
IndiaGHRU_SIR,
NeumannGermany_SIR,
Whitelaw_CHAMPS_SIR,
Oxfordshire_Kpn_SIR,
Oxfordshire_Kvv_SIR,
IZSAM_Italy_SIR,
LaPaz_Lazaro_SIR,
HarrisMERINO_SIR,
PachecoBrazil_SIR) %>%
group_by(Group) %>%
summarise_all(funs(sum(., na.rm = TRUE)))
pooled_NWT_prediction <- matrix(c(subset(pooled_NWT, Group == "pred_S")$S,
subset(pooled_NWT, Group == "pred_S")$I_R,
subset(pooled_NWT, Group == "pred_I_R")$S,
subset(pooled_NWT, Group == "pred_I_R")$I_R), ncol=2, byrow=TRUE)
colnames(pooled_NWT_prediction) <- c('S','I_R')
rownames(pooled_NWT_prediction) <- c('pred_S','pred_I_R')
pooled_NWT_summary<-metrics(pooled_NWT_prediction)TOTAL SIR counts per dataset
SIR_counts_table <- bind_rows(lst(EUSCAPE_EURECA_SIR,
KayamaJapan_SIR,
IndiaGHRU_SIR,
AGAR_SIR,
Oxfordshire_Kpn_SIR,
IZSAM_Italy_SIR,
MBIRA_Kpn_SIR,
HowdenGorrie_SIR,
Milan_SIR,
HRYC_Spain_SIR,
LaPaz_Lazaro_SIR,
MBIRA_Kvv_SIR,
Oxfordshire_Kvv_SIR,
NeumannGermany_SIR,
PachecoBrazil_SIR,
HarrisMERINO_SIR,
FeaseyMalawi_SIR,
EgliSwitzerland_SIR,
HailuEthiopia_SIR,
Whitelaw_CHAMPS_SIR), .id = 'Dataset') %>%
filter(Group!="pred_S_I" & Group!="pred_I_R")
SIR_counts_table$Dataset <- gsub("_SIR",'',SIR_counts_table$Dataset)
SIR_counts_table<-SIR_counts_table %>% select(-Group, -S_I)
S_counts_table <-
SIR_counts_table %>%
group_by(Dataset) %>%
summarise(S_total = sum(S))
R_counts_table <-
SIR_counts_table %>%
group_by(Dataset) %>%
summarise(R_total = sum(R))
IR_counts_table <-
SIR_counts_table %>%
group_by(Dataset) %>%
summarise(IR_total = sum(I_R))
SR_table<- full_join(S_counts_table, R_counts_table)
SIR_table<- full_join(SR_table, IR_counts_table) %>%
mutate(I_total=IR_total-R_total) %>%
mutate(N_total=S_total+IR_total)
SIR_table$Dataset[SIR_table$Dataset == 'AGAR'] <- 'AGAR GnSOP'
SIR_table$Dataset[SIR_table$Dataset == 'EUSCAPE_EURECA'] <- 'EUSCAPE & EURECA'
SIR_table$Dataset[SIR_table$Dataset == 'EgliSwitzerland'] <- 'University of Zurich'
SIR_table$Dataset[SIR_table$Dataset == 'FeaseyMalawi'] <- 'Queen Elizabeth Central Hospital'
SIR_table$Dataset[SIR_table$Dataset == 'HRYC_Spain'] <- 'HURYC & CIBERINFEC'
SIR_table$Dataset[SIR_table$Dataset == 'HailuEthiopia'] <- 'Addis Ababa University*'
SIR_table$Dataset[SIR_table$Dataset == 'HowdenGorrie'] <- 'Controlling Superbugs study & Victorian CPE program'
SIR_table$Dataset[SIR_table$Dataset == 'IZSAM_Italy'] <- 'IZSAM'
SIR_table$Dataset[SIR_table$Dataset == 'IndiaGHRU'] <- 'India GHRU'
SIR_table$Dataset[SIR_table$Dataset == 'KayamaJapan'] <- 'JARBS-GNR'
SIR_table$Dataset[SIR_table$Dataset == 'LaPaz_Lazaro'] <- 'HULP'
SIR_table$Dataset[SIR_table$Dataset == 'LaPaz_Lazaro'] <- 'HULP'
SIR_table$Dataset[SIR_table$Dataset == 'MBIRA_Kpn'] <- 'MBIRA'
SIR_table$Dataset[SIR_table$Dataset == 'MBIRA_Kvv'] <- 'MBIRA*'
SIR_table$Dataset[SIR_table$Dataset == 'Milan'] <- 'IRCCS Ospedale San Raffaele'
SIR_table$Dataset[SIR_table$Dataset == 'NeumannGermany'] <- 'SCHARKI'
SIR_table$Dataset[SIR_table$Dataset == 'Oxfordshire_Kpn'] <- 'Oxfordshire'
SIR_table$Dataset[SIR_table$Dataset == 'Oxfordshire_Kvv'] <- 'Oxfordshire*'
SIR_table$Dataset[SIR_table$Dataset == 'Whitelaw_CHAMPS'] <- 'CHAMPS'Combining validation sets R and NWT
validation_summary <- rbind(EUSCAPE_EURECA_R_prediction_summary$summary,
EUSCAPE_EURECA_NWT_prediction_summary$summary,
KayamaJapan_R_prediction_summary$summary,
KayamaJapan_NWT_prediction_summary$summary,
IndiaGHRU_R_prediction_summary$summary,
IndiaGHRU_NWT_prediction_summary$summary,
AGAR_R_prediction_summary$summary,
AGAR_NWT_prediction_summary$summary,
Oxfordshire_Kpn_R_prediction_summary$summary,
Oxfordshire_Kpn_NWT_prediction_summary$summary,
IZSAM_Italy_R_prediction_summary$summary,
IZSAM_Italy_NWT_prediction_summary$summary,
MBIRA_Kpn_R_prediction_summary$summary,
MBIRA_Kpn_NWT_prediction_summary$summary,
HowdenGorrie_R_prediction_summary$summary,
HowdenGorrie_NWT_prediction_summary$summary,
Milan_R_prediction_summary$summary,
Milan_NWT_prediction_summary$summary,
HRYC_Spain_R_prediction_summary$summary,
HRYC_Spain_NWT_prediction_summary$summary,
LaPaz_Lazaro_R_prediction_summary$summary,
LaPaz_Lazaro_NWT_prediction_summary$summary,
MBIRA_Kvv_R_prediction_summary$summary,
MBIRA_Kvv_NWT_prediction_summary$summary,
Oxfordshire_Kvv_R_prediction_summary$summary,
Oxfordshire_Kvv_NWT_prediction_summary$summary,
NeumannGermany_R_prediction_summary$summary,
NeumannGermany_NWT_prediction_summary$summary,
PachecoBrazil_R_prediction_summary$summary,
PachecoBrazil_NWT_prediction_summary$summary,
HarrisMERINO_R_prediction_summary$summary,
HarrisMERINO_NWT_prediction_summary$summary,
FeaseyMalawi_R_prediction_summary$summary,
FeaseyMalawi_NWT_prediction_summary$summary,
EgliSwitzerland_R_prediction_summary$summary,
EgliSwitzerland_NWT_prediction_summary$summary,
HailuEthiopia_R_prediction_summary$summary,
HailuEthiopia_NWT_prediction_summary$summary,
Whitelaw_CHAMPS_R_prediction_summary$summary,
Whitelaw_CHAMPS_NWT_prediction_summary$summary,
pooled_R_summary$summary,
pooled_NWT_summary$summary)
validation_summary_table <- as_tibble(validation_summary) %>%
mutate(Prediction=c("R", "NWT","R", "NWT","R", "NWT","R", "NWT","R", "NWT", "R", "NWT", "R", "NWT","R", "NWT","R", "NWT","R", "NWT","R", "NWT","R", "NWT","R", "NWT","R", "NWT","R", "NWT","R", "NWT","R", "NWT","R", "NWT","R", "NWT", "R", "NWT","R", "NWT"))%>%
mutate(Dataset=c("EUSCAPE & EURECA", "EUSCAPE & EURECA","JARBS-GNR", "JARBS-GNR", "India GHRU", "India GHRU", "AGAR GnSOP", "AGAR GnSOP","Oxfordshire","Oxfordshire","IZSAM","IZSAM", "MBIRA", "MBIRA", "Controlling Superbugs study & Victorian CPE program", "Controlling Superbugs study & Victorian CPE program", "IRCCS Ospedale San Raffaele", "IRCCS Ospedale San Raffaele", "HURYC & CIBERINFEC", "HURYC & CIBERINFEC", "HULP", "HULP", "MBIRA*", "MBIRA*","Oxfordshire*","Oxfordshire*", "SCHARKI", "SCHARKI", "PachecoBrazil", "PachecoBrazil","HarrisMERINO","HarrisMERINO","Queen Elizabeth Central Hospital", "Queen Elizabeth Central Hospital","University of Zurich", "University of Zurich", "Addis Ababa University*", "Addis Ababa University*", "CHAMPS", "CHAMPS", "Pooled", "Pooled")) %>%
mutate(Country=c("Various European countries", "Various European countries","Japan", "Japan", "India", "India", "Australia", "Australia","United Kingdom","United Kingdom","Italy","Italy","Tanzania, Ethiopia, Malawi, Zambia, Ghana, Nigeria, South Africa", "Tanzania, Ethiopia, Malawi, Zambia, Ghana, Nigeria, South Africa", "Australia", "Australia", "Italy", "Italy", "Spain, Portugal", "Spain, Portugal","Spain", "Spain", "Ethiopia, South Africa, Tanzania", "Ethiopia, South Africa, Tanzania","United Kingdom", "United Kingdom", "Germany", "Germany","Brazil", "Brazil", "Various countries", "Various countries", "Malawi", "Malawi","Switzerland", "Switzerland", "Ethiopia", "Ethiopia", "South Africa", "South Africa", "", "")) %>%
rename(Sensitivity=sens, Specificity=spec, `ME`=me, `VME`=vme, `Categorical agreement`=cat, `%R`=perc_R, N=n) %>%
mutate(Sensitivity=paste0(round(as.numeric(Sensitivity)*100,2),"%")) %>%
mutate(Specificity=paste0(round(as.numeric(Specificity)*100,2),"%")) %>%
mutate(ME=paste0(round(as.numeric(ME)*100,2),"%")) %>%
mutate(VME=paste0(round(as.numeric(VME)*100,2),"%")) %>%
mutate(`Categorical agreement`=paste0(round(as.numeric(`Categorical agreement`)*100,2),"%")) %>%
mutate(`%R`=paste0(round(as.numeric(`%R`)*100,2),"%"))
# %>% select(Prediction, Dataset, Country, `Categorical agreement`, Sensitivity, Specificity, ME, VME, `%R` ,N)
#write_csv(validation_summary_table, "../tables/Table3_extValidation_evalMetrics.csv", na="-")Validaion sets - removing I
MBIRA_Kpn_excludeI_prediction <- matrix(c(subset(MBIRA_Kpn_SIR, Group == "pred_S_I")$S,
subset(MBIRA_Kpn_SIR, Group == "pred_S_I")$R,
subset(MBIRA_Kpn_SIR, Group == "pred_R")$S,
subset(MBIRA_Kpn_SIR, Group == "pred_R")$R), ncol=2, byrow=TRUE)
colnames(MBIRA_Kpn_excludeI_prediction) <- c('S','R')
rownames(MBIRA_Kpn_excludeI_prediction) <- c('pred_S_I','pred_R')
MBIRA_Kpn_excludeI_prediction_summary<-metrics(MBIRA_Kpn_excludeI_prediction)MBIRA_Kvv_excludeI_prediction <- matrix(c(subset(MBIRA_Kvv_SIR, Group == "pred_S_I")$S,
subset(MBIRA_Kvv_SIR, Group == "pred_S_I")$R,
subset(MBIRA_Kvv_SIR, Group == "pred_R")$S,
subset(MBIRA_Kvv_SIR, Group == "pred_R")$R), ncol=2, byrow=TRUE)
colnames(MBIRA_Kvv_excludeI_prediction) <- c('S','R')
rownames(MBIRA_Kvv_excludeI_prediction) <- c('pred_S_I','pred_R')
MBIRA_Kvv_excludeI_prediction_summary<-metrics(MBIRA_Kvv_excludeI_prediction)HowdenGorrie_excludeI_prediction <- matrix(c(subset(HowdenGorrie_SIR, Group == "pred_S_I")$S,
subset(HowdenGorrie_SIR, Group == "pred_S_I")$R,
subset(HowdenGorrie_SIR, Group == "pred_R")$S,
subset(HowdenGorrie_SIR, Group == "pred_R")$R), ncol=2, byrow=TRUE)
colnames(HowdenGorrie_excludeI_prediction) <- c('S','R')
rownames(HowdenGorrie_excludeI_prediction) <- c('pred_S_I','pred_R')
HowdenGorrie_excludeI_prediction_summary<-metrics(HowdenGorrie_excludeI_prediction)AGAR_excludeI_prediction <- matrix(c(subset(AGAR_SIR, Group == "pred_S_I")$S,
subset(AGAR_SIR, Group == "pred_S_I")$R,
subset(AGAR_SIR, Group == "pred_R")$S,
subset(AGAR_SIR, Group == "pred_R")$R), ncol=2, byrow=TRUE)
colnames(AGAR_excludeI_prediction) <- c('S','R')
rownames(AGAR_excludeI_prediction) <- c('pred_S_I','pred_R')
AGAR_excludeI_prediction_summary<-metrics(AGAR_excludeI_prediction)Milan_excludeI_prediction <- matrix(c(subset(Milan_SIR, Group == "pred_S_I")$S,
subset(Milan_SIR, Group == "pred_S_I")$R,
subset(Milan_SIR, Group == "pred_R")$S,
subset(Milan_SIR, Group == "pred_R")$R), ncol=2, byrow=TRUE)
colnames(Milan_excludeI_prediction) <- c('S','R')
rownames(Milan_excludeI_prediction) <- c('pred_S_I','pred_R')
Milan_excludeI_prediction_summary<-metrics(Milan_excludeI_prediction)HailuEthiopia_excludeI_prediction <- matrix(c(subset(HailuEthiopia_SIR, Group == "pred_S_I")$S,
subset(HailuEthiopia_SIR, Group == "pred_S_I")$R,
subset(HailuEthiopia_SIR, Group == "pred_R")$S,
subset(HailuEthiopia_SIR, Group == "pred_R")$R), ncol=2, byrow=TRUE)
colnames(HailuEthiopia_excludeI_prediction) <- c('S','R')
rownames(HailuEthiopia_excludeI_prediction) <- c('pred_S_I','pred_R')
HailuEthiopia_excludeI_prediction_summary<-metrics(HailuEthiopia_excludeI_prediction)KayamaJapan_excludeI_prediction <- matrix(c(subset(KayamaJapan_SIR, Group == "pred_S_I")$S,
subset(KayamaJapan_SIR, Group == "pred_S_I")$R,
subset(KayamaJapan_SIR, Group == "pred_R")$S,
subset(KayamaJapan_SIR, Group == "pred_R")$R), ncol=2, byrow=TRUE)
colnames(KayamaJapan_excludeI_prediction) <- c('S','R')
rownames(KayamaJapan_excludeI_prediction) <- c('pred_S_I','pred_R')
KayamaJapan_excludeI_prediction_summary<-metrics(KayamaJapan_excludeI_prediction)FeaseyMalawi_excludeI_prediction <- matrix(c(subset(FeaseyMalawi_SIR, Group == "pred_S_I")$S,
subset(FeaseyMalawi_SIR, Group == "pred_S_I")$R,
subset(FeaseyMalawi_SIR, Group == "pred_R")$S,
subset(FeaseyMalawi_SIR, Group == "pred_R")$R), ncol=2, byrow=TRUE)
colnames(FeaseyMalawi_excludeI_prediction) <- c('S','R')
rownames(FeaseyMalawi_excludeI_prediction) <- c('pred_S_I','pred_R')
FeaseyMalawi_excludeI_prediction_summary<-metrics(FeaseyMalawi_excludeI_prediction)EUSCAPE_EURECA_excludeI_prediction <- matrix(c(subset(EUSCAPE_EURECA_SIR, Group == "pred_S_I")$S,
subset(EUSCAPE_EURECA_SIR, Group == "pred_S_I")$R,
subset(EUSCAPE_EURECA_SIR, Group == "pred_R")$S,
subset(EUSCAPE_EURECA_SIR, Group == "pred_R")$R), ncol=2, byrow=TRUE)
colnames(EUSCAPE_EURECA_excludeI_prediction) <- c('S','R')
rownames(EUSCAPE_EURECA_excludeI_prediction) <- c('pred_S_I','pred_R')
EUSCAPE_EURECA_excludeI_prediction_summary<-metrics(EUSCAPE_EURECA_excludeI_prediction)EgliSwitzerland_excludeI_prediction <- matrix(c(subset(EgliSwitzerland_SIR, Group == "pred_S_I")$S,
subset(EgliSwitzerland_SIR, Group == "pred_S_I")$R,
subset(EgliSwitzerland_SIR, Group == "pred_R")$S,
subset(EgliSwitzerland_SIR, Group == "pred_R")$R), ncol=2, byrow=TRUE)
colnames(EgliSwitzerland_excludeI_prediction) <- c('S','R')
rownames(EgliSwitzerland_excludeI_prediction) <- c('pred_S_I','pred_R')
EgliSwitzerland_excludeI_prediction_summary<-metrics(EgliSwitzerland_excludeI_prediction)HRYC_Spain_excludeI_prediction <- matrix(c(subset(HRYC_Spain_SIR, Group == "pred_S_I")$S,
subset(HRYC_Spain_SIR, Group == "pred_S_I")$R,
subset(HRYC_Spain_SIR, Group == "pred_R")$S,
subset(HRYC_Spain_SIR, Group == "pred_R")$R), ncol=2, byrow=TRUE)
colnames(HRYC_Spain_excludeI_prediction) <- c('S','R')
rownames(HRYC_Spain_excludeI_prediction) <- c('pred_S_I','pred_R')
HRYC_Spain_excludeI_prediction_summary<-metrics(HRYC_Spain_excludeI_prediction)IndiaGHRU_excludeI_prediction <- matrix(c(subset(IndiaGHRU_SIR, Group == "pred_S_I")$S,
subset(IndiaGHRU_SIR, Group == "pred_S_I")$R,
subset(IndiaGHRU_SIR, Group == "pred_R")$S,
subset(IndiaGHRU_SIR, Group == "pred_R")$R), ncol=2, byrow=TRUE)
colnames(IndiaGHRU_excludeI_prediction) <- c('S','R')
rownames(IndiaGHRU_excludeI_prediction) <- c('pred_S_I','pred_R')
IndiaGHRU_excludeI_prediction_summary<-metrics(IndiaGHRU_excludeI_prediction)NeumannGermany_excludeI_prediction <- matrix(c(subset(NeumannGermany_SIR, Group == "pred_S_I")$S,
subset(NeumannGermany_SIR, Group == "pred_S_I")$R,
subset(NeumannGermany_SIR, Group == "pred_R")$S,
subset(NeumannGermany_SIR, Group == "pred_R")$R), ncol=2, byrow=TRUE)
colnames(NeumannGermany_excludeI_prediction) <- c('S','R')
rownames(NeumannGermany_excludeI_prediction) <- c('pred_S_I','pred_R')
NeumannGermany_excludeI_prediction_summary<-metrics(NeumannGermany_excludeI_prediction)Whitelaw_CHAMPS_excludeI_prediction <- matrix(c(subset(Whitelaw_CHAMPS_SIR, Group == "pred_S_I")$S,
subset(Whitelaw_CHAMPS_SIR, Group == "pred_S_I")$R,
subset(Whitelaw_CHAMPS_SIR, Group == "pred_R")$S,
subset(Whitelaw_CHAMPS_SIR, Group == "pred_R")$R), ncol=2, byrow=TRUE)
colnames(Whitelaw_CHAMPS_excludeI_prediction) <- c('S','R')
rownames(Whitelaw_CHAMPS_excludeI_prediction) <- c('pred_S_I','pred_R')
Whitelaw_CHAMPS_excludeI_prediction_summary<-metrics(Whitelaw_CHAMPS_excludeI_prediction)Oxfordshire_Kpn_excludeI_prediction <- matrix(c(subset(Oxfordshire_Kpn_SIR, Group == "pred_S_I")$S,
subset(Oxfordshire_Kpn_SIR, Group == "pred_S_I")$R,
subset(Oxfordshire_Kpn_SIR, Group == "pred_R")$S,
subset(Oxfordshire_Kpn_SIR, Group == "pred_R")$R), ncol=2, byrow=TRUE)
colnames(Oxfordshire_Kpn_excludeI_prediction) <- c('S','R')
rownames(Oxfordshire_Kpn_excludeI_prediction) <- c('pred_S_I','pred_R')
Oxfordshire_Kpn_excludeI_prediction_summary<-metrics(Oxfordshire_Kpn_excludeI_prediction)Oxfordshire_Kvv_excludeI_prediction <- matrix(c(subset(Oxfordshire_Kvv_SIR, Group == "pred_S_I")$S,
subset(Oxfordshire_Kvv_SIR, Group == "pred_S_I")$R,
subset(Oxfordshire_Kvv_SIR, Group == "pred_R")$S,
subset(Oxfordshire_Kvv_SIR, Group == "pred_R")$R), ncol=2, byrow=TRUE)
colnames(Oxfordshire_Kvv_excludeI_prediction) <- c('S','R')
rownames(Oxfordshire_Kvv_excludeI_prediction) <- c('pred_S_I','pred_R')
Oxfordshire_Kvv_excludeI_prediction_summary<-metrics(Oxfordshire_Kvv_excludeI_prediction)IZSAM_Italy_excludeI_prediction <- matrix(c(subset(IZSAM_Italy_SIR, Group == "pred_S_I")$S,
subset(IZSAM_Italy_SIR, Group == "pred_S_I")$R,
subset(IZSAM_Italy_SIR, Group == "pred_R")$S,
subset(IZSAM_Italy_SIR, Group == "pred_R")$R), ncol=2, byrow=TRUE)
colnames(IZSAM_Italy_excludeI_prediction) <- c('S','R')
rownames(IZSAM_Italy_excludeI_prediction) <- c('pred_S_I','pred_R')
IZSAM_Italy_excludeI_prediction_summary<-metrics(IZSAM_Italy_excludeI_prediction)LaPaz_Lazaro_excludeI_prediction <- matrix(c(subset(LaPaz_Lazaro_SIR, Group == "pred_S_I")$S,
subset(LaPaz_Lazaro_SIR, Group == "pred_S_I")$R,
subset(LaPaz_Lazaro_SIR, Group == "pred_R")$S,
subset(LaPaz_Lazaro_SIR, Group == "pred_R")$R), ncol=2, byrow=TRUE)
colnames(LaPaz_Lazaro_excludeI_prediction) <- c('S','R')
rownames(LaPaz_Lazaro_excludeI_prediction) <- c('pred_S_I','pred_R')
LaPaz_Lazaro_excludeI_prediction_summary<-metrics(LaPaz_Lazaro_excludeI_prediction)HarrisMERINO_excludeI_prediction <- matrix(c(subset(HarrisMERINO_SIR, Group == "pred_S_I")$S,
subset(HarrisMERINO_SIR, Group == "pred_S_I")$R,
subset(HarrisMERINO_SIR, Group == "pred_R")$S,
subset(HarrisMERINO_SIR, Group == "pred_R")$R), ncol=2, byrow=TRUE)
colnames(HarrisMERINO_excludeI_prediction) <- c('S','R')
rownames(HarrisMERINO_excludeI_prediction) <- c('pred_S_I','pred_R')
HarrisMERINO_excludeI_prediction_summary<-metrics(HarrisMERINO_excludeI_prediction)PachecoBrazil_excludeI_prediction <- matrix(c(subset(PachecoBrazil_SIR, Group == "pred_S_I")$S,
subset(PachecoBrazil_SIR, Group == "pred_S_I")$R,
subset(PachecoBrazil_SIR, Group == "pred_R")$S,
subset(PachecoBrazil_SIR, Group == "pred_R")$R), ncol=2, byrow=TRUE)
colnames(PachecoBrazil_excludeI_prediction) <- c('S','R')
rownames(PachecoBrazil_excludeI_prediction) <- c('pred_S_I','pred_R')
PachecoBrazil_excludeI_prediction_summary<-metrics(PachecoBrazil_excludeI_prediction)pooled_exclude_I <-
bind_rows(MBIRA_Kpn_SIR,
MBIRA_Kvv_SIR,
HowdenGorrie_SIR,
AGAR_SIR,
Milan_SIR,
HailuEthiopia_SIR,
KayamaJapan_SIR,
FeaseyMalawi_SIR,
EUSCAPE_EURECA_SIR,
EgliSwitzerland_SIR,
HRYC_Spain_SIR,
IndiaGHRU_SIR,
NeumannGermany_SIR,
Whitelaw_CHAMPS_SIR,
Oxfordshire_Kpn_SIR,
Oxfordshire_Kvv_SIR,
IZSAM_Italy_SIR,
LaPaz_Lazaro_SIR,
HarrisMERINO_SIR,
PachecoBrazil_SIR) %>%
group_by(Group) %>%
summarise_all(funs(sum(., na.rm = TRUE)))
pooled_excludeI_prediction <- matrix(c(subset(pooled_exclude_I, Group == "pred_S_I")$S,
subset(pooled_exclude_I, Group == "pred_S_I")$R,
subset(pooled_exclude_I, Group == "pred_R")$S,
subset(pooled_exclude_I, Group == "pred_R")$R), ncol=2, byrow=TRUE)
colnames(pooled_excludeI_prediction) <- c('S','R')
rownames(pooled_excludeI_prediction) <- c('pred_S_I','pred_R')
pooled_excludeI_summary<-metrics(pooled_excludeI_prediction)Combining validation sets - excluding I
validation_summary <- rbind(
EUSCAPE_EURECA_excludeI_prediction_summary$summary,
KayamaJapan_excludeI_prediction_summary$summary,
IndiaGHRU_excludeI_prediction_summary$summary,
AGAR_excludeI_prediction_summary$summary,
Oxfordshire_Kpn_excludeI_prediction_summary$summary,
IZSAM_Italy_excludeI_prediction_summary$summary,
MBIRA_Kpn_excludeI_prediction_summary$summary,
HowdenGorrie_excludeI_prediction_summary$summary,
Milan_excludeI_prediction_summary$summary,
HRYC_Spain_excludeI_prediction_summary$summary,
LaPaz_Lazaro_excludeI_prediction_summary$summary,
MBIRA_Kvv_excludeI_prediction_summary$summary,
Oxfordshire_Kvv_excludeI_prediction_summary$summary,
NeumannGermany_excludeI_prediction_summary$summary,
PachecoBrazil_excludeI_prediction_summary$summary,
HarrisMERINO_excludeI_prediction_summary$summary,
FeaseyMalawi_excludeI_prediction_summary$summary,
EgliSwitzerland_excludeI_prediction_summary$summary,
HailuEthiopia_excludeI_prediction_summary$summary,
Whitelaw_CHAMPS_excludeI_prediction_summary$summary,
pooled_excludeI_summary$summary)
validation_summary_table_excludeI <- as_tibble(validation_summary) %>%
mutate(Prediction=c("R_excludeI", "R_excludeI","R_excludeI", "R_excludeI","R_excludeI", "R_excludeI","R_excludeI", "R_excludeI","R_excludeI", "R_excludeI", "R_excludeI", "R_excludeI", "R_excludeI", "R_excludeI","R_excludeI", "R_excludeI","R_excludeI", "R_excludeI", "R_excludeI","R_excludeI", "R_excludeI"))%>%
mutate(Dataset=c("EUSCAPE & EURECA","JARBS-GNR", "India GHRU", "AGAR GnSOP","Oxfordshire","IZSAM", "MBIRA", "Controlling Superbugs study & Victorian CPE program", "IRCCS Ospedale San Raffaele", "HURYC & CIBERINFEC", "HULP", "MBIRA*","Oxfordshire*", "SCHARKI","PachecoBrazil","HarrisMERINO", "Queen Elizabeth Central Hospital", "University of Zurich", "Addis Ababa University*", "CHAMPS", "Pooled")) %>%
mutate(Country=c("Various European countries","Japan", "India", "Australia","United Kingdom","Italy","Tanzania, Ethiopia, Malawi, Zambia, Ghana, Nigeria, South Africa", "Australia", "Italy","Spain, Portugal", "Spain", "Ethiopia, South Africa, Tanzania" ,"United Kingdom", "Germany", "Brazil", "Various countries", "Malawi","Switzerland", "Ethiopia", "South Africa", "")) %>%
rename(Sensitivity=sens, Specificity=spec, `ME`=me, `VME`=vme, `Categorical agreement`=cat, `%R`=perc_R, N=n) %>%
mutate(Sensitivity=paste0(round(as.numeric(Sensitivity)*100,2),"%")) %>%
mutate(Specificity=paste0(round(as.numeric(Specificity)*100,2),"%")) %>%
mutate(ME=paste0(round(as.numeric(ME)*100,2),"%")) %>%
mutate(VME=paste0(round(as.numeric(VME)*100,2),"%")) %>%
mutate(`Categorical agreement`=paste0(round(as.numeric(`Categorical agreement`)*100,2),"%")) %>%
mutate(`%R`=paste0(round(as.numeric(`%R`)*100,2),"%"))
#%>% select(Prediction, Dataset, Country, `Categorical agreement`, Sensitivity, Specificity, ME, VME, `%R` ,N)
#write_csv(validation_summary_table_excludeI, "../tables/Table3_extValidation_evalMetrics_excludeI.csv", na="-")Plotting validation sets (only R_excludeI)
validation_summary_table_long<-gather(validation_summary_table_excludeI, `Evaluation metric`, value, `Categorical agreement`:`VME`)
validation_summary_table_long<- validation_summary_table_long %>% mutate(value = as.numeric(gsub("%", "", value))) %>% rename(metric_value=value)
validation_summary_table_long_ci<-
validation_summary_table_long %>%
gather(v, value, cat_lb:vme_ub) %>%
separate(v, c("var", "col")) %>%
arrange(Dataset) %>%
spread(col, value)
validation_summary_table_long_ci <-
validation_summary_table_long_ci %>%
filter(`Evaluation metric`=="Categorical agreement" & var =="cat" |
`Evaluation metric`=="ME" & var =="me" |
`Evaluation metric`=="VME" & var =="vme") %>%
filter(Prediction=="R_excludeI")
validation_summary_table_long_ci$`Evaluation metric` = factor(validation_summary_table_long_ci$`Evaluation metric`, levels=c("Categorical agreement","ME", 'VME'))
#validation_summary_table_median<-
#validation_summary_table_long_ci %>% group_by(`Evaluation metric`) %>%
# summarise(metric_value = median(as.numeric(metric_value), na.rm = T)) %>%
# mutate(Dataset="Overall median") %>%
# mutate(var="") %>%
# mutate(ub="") %>%
# mutate(lb="") %>%
# mutate(Country="") %>%
# mutate(N="") %>%
# mutate(`%R`="") %>%
# mutate(`Prediction`="")
#validation_summary_table_total <-
#rbind(validation_summary_table_long_ci)
validation_summary_table_total <- full_join(validation_summary_table_long_ci, SIR_table) %>%
mutate(Dataset = (gsub("Pooled", "All datasets combined", Dataset)))%>%
mutate(Dataset_label=paste(Dataset, " (S=", S_total, ", R=",R_total, ")", sep = ""))
#validation_summary_table_total$Dataset_label[validation_summary_table_total$Dataset_label == 'Overall median (S=NA, R=NA)'] <- 'Overall median'
# Get totals to explicitly write in label
colSums(SIR_table[,-1])## S_total R_total IR_total I_total N_total
## 1665 4462 5094 632 6759validation_summary_table_total$Dataset_label[validation_summary_table_total$Dataset_label == 'All datasets combined (S=NA, R=NA)'] <- "All datasets combined (S=1665, R=4462)"
validation_summary_table_total<- validation_summary_table_total %>%
mutate(Dataset_label = (gsub("EUSCAPE & EURECA", "A (EUCAST)", Dataset_label)))%>%
mutate(Dataset_label = (gsub("India GHRU", "B (EUCAST)", Dataset_label)))%>%
mutate(Dataset_label = (gsub("AGAR GnSOP", "C (EUCAST)", Dataset_label)))%>%
mutate(Dataset_label = (gsub("Oxfordshire\\*", "XXXXXXX", Dataset_label)))%>%
mutate(Dataset_label = (gsub("Oxfordshire", "D (EUCAST)", Dataset_label)))%>%
mutate(Dataset_label = (gsub("XXXXXXX", "Oxfordshire (Kvv, EUCAST)", Dataset_label)))%>%
mutate(Dataset_label = (gsub("HURYC & CIBERINFEC", "E (EUCAST)", Dataset_label)))%>%
mutate(Dataset_label = (gsub("HULP", "F (EUCAST)", Dataset_label)))%>%
mutate(Dataset_label = (gsub("JARBS-GNR", "G (CLSI)", Dataset_label)))%>%
mutate(Dataset_label = (gsub("MBIRA\\*", "YYYYYYYY", Dataset_label)))%>%
mutate(Dataset_label = (gsub("MBIRA", "H (CLSI)", Dataset_label)))%>%
mutate(Dataset_label = (gsub("YYYYYYYY", "MBIRA (Kvv, CLSI)", Dataset_label)))%>%
mutate(Dataset_label = (gsub("Controlling Superbugs study & Victorian CPE program", "I (CLSI)", Dataset_label)))%>%
mutate(Dataset_label = (gsub("Queen Elizabeth Central Hospital", "J* (EUCAST/BSAC)", Dataset_label)))%>%
mutate(Dataset_label = (gsub("Addis Ababa University\\*", "K* (Kvv, CLSI)", Dataset_label)))%>%
mutate(Dataset_label = (gsub("University of Zurich", "L* (EUCAST)", Dataset_label)))%>%
mutate(Dataset_label = (gsub("CHAMPS", "M* (CLSI)", Dataset_label)))
validation_summary_table_total$Dataset_label <- factor(as.character(validation_summary_table_total$Dataset_label), levels = c("A (EUCAST) (S=291, R=1687)", "B (EUCAST) (S=84, R=441)","C (EUCAST) (S=168, R=255)", "D (EUCAST) (S=242, R=37)", "E (EUCAST) (S=20, R=146)", "F (EUCAST) (S=11, R=136)", "G (CLSI) (S=222, R=449)","H (CLSI) (S=113, R=129)", "I (CLSI) (S=18, R=153)", "J* (EUCAST/BSAC) (S=196, R=411)","K* (Kvv, CLSI) (S=37, R=28)","L* (EUCAST) (S=11, R=53)", "M* (CLSI) (S=18, R=21)", "IZSAM (S=7, R=273)", "IRCCS Ospedale San Raffaele (S=9, R=187)", "MBIRA (Kvv, CLSI) (S=86, R=5)", "Oxfordshire (Kvv, EUCAST) (S=64, R=0)", "SCHARKI (S=51, R=2)", "PachecoBrazil (S=10, R=28)", "HarrisMERINO (S=7, R=21)","All datasets combined (S=1665, R=4462)", "Overall median"))
validation_summary_table_total_exclude <-
validation_summary_table_total %>%
filter(S_total>10 | is.na(S_total)) %>%
filter(R_total>10 | is.na(R_total)) %>%
filter(N_total>25 | is.na(N_total))
validation_summary_table_median<-
validation_summary_table_total_exclude %>% group_by(`Evaluation metric`) %>%
summarise(metric_value = median(as.numeric(metric_value), na.rm = T)) %>%
mutate(Dataset="Overall median") %>%
mutate(var="") %>%
mutate(ub="") %>%
mutate(lb="") %>%
mutate(Country="") %>%
mutate(N="") %>%
mutate(`%R`="") %>%
mutate(`Prediction`="")%>%
mutate(`S_total`="")%>%
mutate(`I_total`="")%>%
mutate(`IR_total`="")%>%
mutate(`R_total`="")%>%
mutate(`N_total`="")%>%
mutate(`Dataset_label`="Overall median")
validation_summary_table_total_exclude <-
rbind(validation_summary_table_total_exclude, validation_summary_table_median)
validation_summary_table_total_exclude$lb[validation_summary_table_total_exclude$lb<0] <- 0
validation_summary_table_plot <-ggplot(validation_summary_table_total_exclude, aes(as.numeric(metric_value),(Dataset_label))) +
geom_point(aes(), alpha=0.5, col="#9f98e9", size=2) +
geom_linerange(aes(xmin=as.numeric(lb)*100, xmax=as.numeric(ub)*100), col="#887feb", position=pd, size=0.5) +
scale_y_discrete(limits=rev)+
scale_x_continuous(breaks=seq(0, 100, 20)) +
facet_grid(.~`Evaluation metric`, scales = "free", space='free')+
labs(y="", x = "%") +
theme_bw()+
theme(legend.title=element_blank())+
theme(axis.text.x = element_text(angle = 45, hjust=1)) +
geom_vline(data=filter(validation_summary_table_total_exclude, `Evaluation metric`=="Categorical agreement"), aes(xintercept=90), colour="#fb9472")+
geom_vline(data=filter(validation_summary_table_total_exclude, `Evaluation metric`=="ME"), aes(xintercept=3), colour="#fb9472")+
geom_vline(data=filter(validation_summary_table_total_exclude, `Evaluation metric`=="VME"), aes(xintercept=1.5), colour="#fb9472")
ggsave(width=7, height=4, file="../figs/Fig7_extValidation_summary.pdf")
ggsave(width=7, height=4, file="../figs/Fig7_extValidation_summary.png")TableS10 - eval metrics
all_datasets_metrics <-
#Below includes NWT + R (including I) + R (excluding I)
rbind(validation_summary_table, validation_summary_table_excludeI) %>%
#Below includes only R (excluding I)
#rbind(validation_summary_table_excludeI) %>%
mutate(`Categorical_agreement_conf_int_lower_bound`=paste0(round(as.numeric(cat_lb)*100,2),"%")) %>%
mutate(`Categorical_agreement_conf_int_upper_bound`=paste0(round(as.numeric(cat_ub)*100,2),"%")) %>%
mutate(`ME_conf_int_lower_bound`=paste0(round(as.numeric(me_lb)*100,2),"%")) %>%
mutate(`ME_conf_int_upper_bound`=paste0(round(as.numeric(me_ub)*100,2),"%")) %>%
mutate(`VME_conf_int_lower_bound`=paste0(round(as.numeric(vme_lb)*100,2),"%")) %>%
mutate(`VME_conf_int_upper_bound`=paste0(round(as.numeric(vme_ub)*100,2),"%")) %>%
mutate(`Sensitivity_conf_int_lower_bound`=paste0(round(as.numeric(sens_lb)*100,2),"%")) %>%
mutate(`Sensitivity_conf_int_upper_bound`=paste0(round(as.numeric(sens_ub)*100,2),"%")) %>%
mutate(`Specificity_conf_int_lower_bound`=paste0(round(as.numeric(spec_lb)*100,2),"%")) %>%
mutate(`Specificity_conf_int_upper_bound`=paste0(round(as.numeric(spec_ub)*100,2),"%"))
all_datasets_metrics <- full_join(all_datasets_metrics, SIR_table) %>% select (-c(IR_total, N_total)) %>%
rename(Susceptible=S_total)%>%
rename(Intermediate=I_total)%>%
rename(Resistant=R_total)
all_datasets_metrics <- all_datasets_metrics[, c("Dataset", "N","Susceptible", "Intermediate", "Resistant", "Prediction", "%R", "Categorical agreement", "Categorical_agreement_conf_int_lower_bound", "Categorical_agreement_conf_int_upper_bound", "ME", "ME_conf_int_lower_bound", "ME_conf_int_upper_bound", "VME", "VME_conf_int_lower_bound", "VME_conf_int_upper_bound", "Sensitivity", "Sensitivity_conf_int_lower_bound", "Sensitivity_conf_int_upper_bound", "Specificity", "Specificity_conf_int_lower_bound", "Specificity_conf_int_upper_bound")]
all_datasets_metrics<- all_datasets_metrics %>%
mutate(Dataset = (gsub("EUSCAPE & EURECA", "A (EUCAST)", Dataset)))%>%
mutate(Dataset = (gsub("India GHRU", "B (EUCAST)", Dataset)))%>%
mutate(Dataset = (gsub("AGAR GnSOP", "C (EUCAST)", Dataset)))%>%
mutate(Dataset = (gsub("Oxfordshire\\*", "XXXXXXX", Dataset)))%>%
mutate(Dataset = (gsub("Oxfordshire", "D (EUCAST)", Dataset)))%>%
mutate(Dataset = (gsub("XXXXXXX", "Oxfordshire (Kvv, EUCAST)", Dataset)))%>%
mutate(Dataset = (gsub("HURYC & CIBERINFEC", "E (EUCAST)", Dataset)))%>%
mutate(Dataset = (gsub("HULP", "F (EUCAST)", Dataset)))%>%
mutate(Dataset = (gsub("JARBS-GNR", "G (CLSI)", Dataset)))%>%
mutate(Dataset = (gsub("MBIRA\\*", "YYYYYYYY", Dataset)))%>%
mutate(Dataset = (gsub("MBIRA", "H (CLSI)", Dataset)))%>%
mutate(Dataset = (gsub("YYYYYYYY", "MBIRA (Kvv, CLSI)", Dataset)))%>%
mutate(Dataset = (gsub("Controlling Superbugs study & Victorian CPE program", "I (CLSI)", Dataset)))%>%
mutate(Dataset = (gsub("Queen Elizabeth Central Hospital", "J* (EUCAST/BSAC)", Dataset)))%>%
mutate(Dataset = (gsub("Addis Ababa University\\*", "K* (Kvv, CLSI)", Dataset)))%>%
mutate(Dataset = (gsub("University of Zurich", "L* (EUCAST)", Dataset)))%>%
mutate(Dataset = (gsub("CHAMPS", "M* (CLSI)", Dataset)))
all_datasets_metrics <- all_datasets_metrics %>%
mutate_all(funs(str_replace(., "NaN%", "-")))
write_tsv(all_datasets_metrics, "../tables/TableS10_extValidation_evalMetrics.tsv", na="-")#Table S11 - genotype profiles vs. PPVs and median MICs
MIC prediction
discrete_quantile <- function(x, p) {
if (length(x)>5){
sort(x)[length(x)*p]
} else return(0)}
validation_geno <- read_delim("../tables/TableS4_genotypes_validation.tsv")
validation_pheno <- read_delim("../tables/TableS5_phenotype_metadata_validation.tsv")
validation <- full_join(validation_geno, validation_pheno)
#MIC median - with I excluded
MIC_pred_excludeI <- validation%>%
filter(Laboratory.Typing.Method != "Disk diffusion") %>%
filter(Resistance.phenotype!="I") %>%
group_by(Dataset,cipro_prediction_group) %>%
summarise(median=median(Measurement),
lower25=discrete_quantile(as.numeric(Measurement), p=0.25),
upper25=discrete_quantile(as.numeric(Measurement), p=0.75)) %>% mutate(IQR_excludeI=paste0(median, " [", lower25, "-", upper25, "]"))
MIC_pred_excludeI[grep("0-0", MIC_pred_excludeI$IQR_excludeI), "IQR_excludeI"] <- "-"
MIC_pred_excludeI$lower25[MIC_pred_excludeI$lower25==0] <- "-"
MIC_pred_excludeI$lower25[MIC_pred_excludeI$upper25==0] <- "-"
MIC_pred_excludeI <- MIC_pred_excludeI %>% rename(Group=cipro_prediction_group) %>%
select (-c(median,lower25,upper25))
MIC_pred_excludeI_pooled <- validation%>%
filter(Laboratory.Typing.Method != "Disk diffusion") %>%
filter(Resistance.phenotype!="I") %>%
group_by(cipro_prediction_group) %>%
summarise(median=median(Measurement),
lower25=discrete_quantile(as.numeric(Measurement), p=0.25),
upper25=discrete_quantile(as.numeric(Measurement), p=0.75)) %>% mutate(IQR_excludeI=paste0(median, " [", lower25, "-", upper25, "]")) %>%
mutate(Dataset=c("Pooled", "Pooled","Pooled", "Pooled","Pooled", "Pooled","Pooled", "Pooled","Pooled", "Pooled"))
MIC_pred_excludeI_pooled[grep("0-0", MIC_pred_excludeI_pooled$IQR_excludeI), "IQR_excludeI"] <- "-"
MIC_pred_excludeI_pooled$lower25[MIC_pred_excludeI_pooled$lower25==0] <- "-"
MIC_pred_excludeI_pooled$lower25[MIC_pred_excludeI_pooled$upper25==0] <- "-"
MIC_pred_excludeI_pooled <- MIC_pred_excludeI_pooled %>% rename(Group=cipro_prediction_group) %>%
select (-c(median,lower25,upper25))
MIC_pred_excludeI <- rbind(MIC_pred_excludeI, MIC_pred_excludeI_pooled)
#MIC median - all included
MIC_pred <- validation%>%
filter(Laboratory.Typing.Method != "Disk diffusion") %>%
group_by(Dataset,cipro_prediction_group) %>%
summarise(median=median(Measurement),
lower25=discrete_quantile(as.numeric(Measurement), p=0.25),
upper25=discrete_quantile(as.numeric(Measurement), p=0.75)) %>% mutate(IQR=paste0(median, " [", lower25, "-", upper25, "]"))
MIC_pred[grep("0-0", MIC_pred$IQR), "IQR"] <- "-"
MIC_pred$lower25[MIC_pred$lower25==0] <- "-"
MIC_pred$lower25[MIC_pred$upper25==0] <- "-"
MIC_pred <- MIC_pred %>% rename(Group=cipro_prediction_group) %>%
select (-c(median,lower25,upper25))
MIC_pred_SIR_pooled <- validation%>%
filter(Laboratory.Typing.Method != "Disk diffusion") %>%
group_by(cipro_prediction_group) %>%
summarise(median=median(Measurement),
lower25=discrete_quantile(as.numeric(Measurement), p=0.25),
upper25=discrete_quantile(as.numeric(Measurement), p=0.75)) %>% mutate(IQR=paste0(median, " [", lower25, "-", upper25, "]")) %>%
mutate(Dataset=c("Pooled", "Pooled","Pooled", "Pooled","Pooled", "Pooled","Pooled", "Pooled","Pooled", "Pooled"))
MIC_pred_SIR_pooled[grep("0-0", MIC_pred_SIR_pooled$IQR), "IQR"] <- "-"
MIC_pred_SIR_pooled$lower25[MIC_pred_SIR_pooled$lower25==0] <- "-"
MIC_pred_SIR_pooled$lower25[MIC_pred_SIR_pooled$upper25==0] <- "-"
MIC_pred_SIR_pooled <- MIC_pred_SIR_pooled %>% rename(Group=cipro_prediction_group) %>%
select (-c(median,lower25,upper25))
MIC_pred <- rbind(MIC_pred, MIC_pred_SIR_pooled)#genotype profiles
pooled_dataset_genotype <-
bind_rows(lst(MBIRA_Kpn_genotype,
MBIRA_Kvv_genotype,
HowdenGorrie_genotype,
AGAR_genotype,
Milan_genotype,
HailuEthiopia_genotype,
KayamaJapan_genotype,
FeaseyMalawi_genotype,
EUSCAPE_EURECA_genotype,
EgliSwitzerland_genotype,
HRYC_Spain_genotype,
IndiaGHRU_genotype,
NeumannGermany_genotype,
Whitelaw_CHAMPS_genotype,
Oxfordshire_Kpn_genotype,
Oxfordshire_Kvv_genotype,
IZSAM_Italy_genotype,
LaPaz_Lazaro_genotype,
HarrisMERINO_genotype,
PachecoBrazil_genotype,
pooled_genotype), .id ="Dataset") %>%
mutate(Dataset = (gsub("_genotype", "", Dataset))) %>%
mutate(N=S+I+R) %>%
mutate(RexcludeI_PPV = paste0(R,"/",(S+R), " (", round(R/(S+R)*100, 2), "%)")) %>%
mutate(R_PPV = paste0(R,"/",(S+I+R), " (", round(R/(S+I+R)*100, 2), "%)")) %>%
mutate(NWT_PPV = paste0((R+I),"/",(S+I+R), " (", round((R+I)/(S+I+R)*100, 2), "%)")) %>%
select (-c(S,I,R,N))
pooled_dataset_genotype$RexcludeI_PPV[pooled_dataset_genotype$RexcludeI_PPV == '0/0 (NaN%)'] <- '-'
pooled_dataset_genotype$R_PPV[pooled_dataset_genotype$R_PPV == '0/0 (NaN%)'] <- '-'
pooled_dataset_genotype$NWT_PPV[pooled_dataset_genotype$NWT_PPV == '0/0 (NaN%)'] <- '-'
pooled_dataset_genotype$Dataset[pooled_dataset_genotype$Dataset == 'AGAR'] <- 'AGAR GnSOP'
pooled_dataset_genotype$Dataset[pooled_dataset_genotype$Dataset == 'EUSCAPE_EURECA'] <- 'EUSCAPE & EURECA'
pooled_dataset_genotype$Dataset[pooled_dataset_genotype$Dataset == 'EgliSwitzerland'] <- 'University of Zurich'
pooled_dataset_genotype$Dataset[pooled_dataset_genotype$Dataset == 'FeaseyMalawi'] <- 'Queen Elizabeth Central Hospital'
pooled_dataset_genotype$Dataset[pooled_dataset_genotype$Dataset == 'HRYC_Spain'] <- 'HURYC & CIBERINFEC'
pooled_dataset_genotype$Dataset[pooled_dataset_genotype$Dataset == 'HailuEthiopia'] <- 'Addis Ababa University*'
pooled_dataset_genotype$Dataset[pooled_dataset_genotype$Dataset == 'HowdenGorrie'] <- 'Controlling Superbugs study & Victorian CPE program'
pooled_dataset_genotype$Dataset[pooled_dataset_genotype$Dataset == 'IZSAM_Italy'] <- 'IZSAM'
pooled_dataset_genotype$Dataset[pooled_dataset_genotype$Dataset == 'IndiaGHRU'] <- 'India GHRU'
pooled_dataset_genotype$Dataset[pooled_dataset_genotype$Dataset == 'KayamaJapan'] <- 'JARBS-GNR'
pooled_dataset_genotype$Dataset[pooled_dataset_genotype$Dataset == 'LaPaz_Lazaro'] <- 'HULP'
pooled_dataset_genotype$Dataset[pooled_dataset_genotype$Dataset == 'LaPaz_Lazaro'] <- 'HULP'
pooled_dataset_genotype$Dataset[pooled_dataset_genotype$Dataset == 'MBIRA_Kpn'] <- 'MBIRA'
pooled_dataset_genotype$Dataset[pooled_dataset_genotype$Dataset == 'MBIRA_Kvv'] <- 'MBIRA*'
pooled_dataset_genotype$Dataset[pooled_dataset_genotype$Dataset == 'Milan'] <- 'IRCCS Ospedale San Raffaele'
pooled_dataset_genotype$Dataset[pooled_dataset_genotype$Dataset == 'NeumannGermany'] <- 'SCHARKI'
pooled_dataset_genotype$Dataset[pooled_dataset_genotype$Dataset == 'Oxfordshire_Kpn'] <- 'Oxfordshire'
pooled_dataset_genotype$Dataset[pooled_dataset_genotype$Dataset == 'Oxfordshire_Kvv'] <- 'Oxfordshire*'
pooled_dataset_genotype$Dataset[pooled_dataset_genotype$Dataset == 'Whitelaw_CHAMPS'] <- 'CHAMPS'
pooled_dataset_genotype$Dataset[pooled_dataset_genotype$Dataset == 'pooled'] <- 'Pooled'
geno_MIC <- merge(pooled_dataset_genotype, MIC_pred, by=c("Dataset","Group"))
geno_MIC <- merge(geno_MIC, MIC_pred_excludeI, by=c("Dataset","Group"))
geno_MIC<- geno_MIC %>%
mutate(Dataset = (gsub("EUSCAPE & EURECA", "A (EUCAST)", Dataset)))%>%
mutate(Dataset = (gsub("India GHRU", "B (EUCAST)", Dataset)))%>%
mutate(Dataset = (gsub("AGAR GnSOP", "C (EUCAST)", Dataset)))%>%
mutate(Dataset = (gsub("Oxfordshire\\*", "XXXXXXX", Dataset)))%>%
mutate(Dataset = (gsub("Oxfordshire", "D (EUCAST)", Dataset)))%>%
mutate(Dataset = (gsub("XXXXXXX", "Oxfordshire (Kvv, EUCAST)", Dataset)))%>%
mutate(Dataset = (gsub("HURYC & CIBERINFEC", "E (EUCAST)", Dataset)))%>%
mutate(Dataset = (gsub("HULP", "F (EUCAST)", Dataset)))%>%
mutate(Dataset = (gsub("JARBS-GNR", "G (CLSI)", Dataset)))%>%
mutate(Dataset = (gsub("MBIRA\\*", "YYYYYYYY", Dataset)))%>%
mutate(Dataset = (gsub("MBIRA", "H (CLSI)", Dataset)))%>%
mutate(Dataset = (gsub("YYYYYYYY", "MBIRA (Kvv, CLSI)", Dataset)))%>%
mutate(Dataset = (gsub("Controlling Superbugs study & Victorian CPE program", "I (CLSI)", Dataset)))%>%
mutate(Dataset = (gsub("Queen Elizabeth Central Hospital", "J* (EUCAST/BSAC)", Dataset)))%>%
mutate(Dataset = (gsub("Addis Ababa University\\*", "K* (Kvv, CLSI)", Dataset)))%>%
mutate(Dataset = (gsub("University of Zurich", "L* (EUCAST)", Dataset)))%>%
mutate(Dataset = (gsub("CHAMPS", "M* (CLSI)", Dataset)))
geno_MIC <- geno_MIC %>%
rename("MIC median [IQR] (exclude I)"=IQR_excludeI) %>%
rename("MIC median [IQR]"=IQR)
write_tsv(geno_MIC, "../tables/TableS11_extValidation_genotypeProfiles.tsv", na="-")#showing R, NWT, R_excludeI
validation_summary_table_combined<-rbind(validation_summary_table, validation_summary_table_excludeI)
validation_summary_table_combined_long<-gather(validation_summary_table_combined, `Evaluation metric`, value, `Categorical agreement`:`%R`)
validation_summary_table_combined_long<- validation_summary_table_combined_long %>% mutate(value = as.numeric(gsub("%", "", value))) %>%
mutate(`Evaluation metric` = (gsub("%R", "%R/NWT", `Evaluation metric`)))
validation_summary_table_combined_long$`Evaluation metric` = factor(validation_summary_table_combined_long$`Evaluation metric`, levels=c("Categorical agreement",'Sensitivity','Specificity',"ME", 'VME', "%R/NWT"))
validation_summary_table_median<-
validation_summary_table_combined_long %>% group_by(Prediction, `Evaluation metric`) %>%
summarise(value = median(as.numeric(value), na.rm = T)) %>%
mutate(Dataset="Overall median") %>%
mutate(Country="") %>%
mutate(N="")
validation_summary_table_total <-
rbind(validation_summary_table_combined_long, validation_summary_table_median)
validation_summary_table_total <- full_join(validation_summary_table_total, SIR_table) %>%
mutate(Dataset = (gsub("Pooled", "All datasets combined", Dataset)))%>%
mutate(Dataset_label=paste(Dataset, " (S=", S_total, ", I=", I_total, ", R=",R_total, ")", sep = ""))
validation_summary_table_total$Dataset_label[validation_summary_table_total$Dataset_label == 'Overall median (S=NA, I=NA, R=NA)'] <- 'Overall median'
# Get totals to explicitly write in label
colSums(SIR_table[,-1])
validation_summary_table_total$Dataset_label[validation_summary_table_total$Dataset_label == 'All datasets combined (S=NA, I=NA, R=NA)'] <- "All datasets combined (S=1648, I=624, R=4413)"
validation_summary_table_total$Dataset_label <- factor(validation_summary_table_total$Dataset_label, levels = c("EUSCAPE & EURECA (S=291, I=155, R=1687)", "JARBS-GNR (S=222, I=163, R=449)", "Queen Elizabeth Central Hospital (S=196, I=130, R=411)", "India GHRU (S=84, I=11, R=441)", "AGAR GnSOP (S=168, I=62, R=255)", "Oxfordshire (S=242, I=16, R=37)", "IZSAM (S=7, I=3, R=273)", "MBIRA (S=113, I=14, R=129)", "Controlling Superbugs study & Victorian CPE program (S=18, I=41, R=153)", "IRCCS Ospedale San Raffaele (S=9, I=2, R=187)", "HURYC & CIBERINFEC (S=20, I=7, R=146)", "HULP (S=11, I=1, R=136)", "MBIRA* (S=86, I=0, R=5)", "University of Zurich (S=11, I=8, R=53)", "Addis Ababa University* (S=37, I=5, R=28)", "Oxfordshire* (S=64, I=2, R=0)", "SCHARKI (S=51, I=3, R=2)", "CHAMPS (S=18, I=1, R=21)","All datasets combined (S=1648, I=624, R=4413)", "Overall median"))
validation_summary_table_plot <-ggplot(validation_summary_table_total, aes(as.numeric(value),fct_inorder(as.factor(Dataset_label)))) +
geom_point(aes(shape = `Prediction`, size=`Prediction`, colour=`Prediction`), alpha=0.5) +
scale_shape_manual(values = c(R=15, NWT=18, R_excludeI=20)) +
scale_size_manual(values = c(R=3, NWT=3, R_excludeI=3)) +
scale_colour_manual(values=c(R="#bd1515", NWT="#2c15ae", R_excludeI="#339966")) +
scale_y_discrete(limits=rev)+
facet_grid(`Evaluation metric`~., scales = "free", space='free')+
labs(y="", x = "%") +
theme_bw()+
theme(legend.title=element_blank())
ggsave(width=10, height=14, file="../figs/FigX_extValidation.pdf")
# Only include datasets with at least 10 S genomes and 10 R genomes, >25 genomes in total, <100% S/R genomes
validation_summary_table_total_exclude <-
validation_summary_table_total %>%
filter(S_total>10 | is.na(S_total)) %>%
filter(R_total>10 | is.na(R_total)) %>%
filter(N>25 | is.na(R_total))
validation_summary_table_plot_exclude <-ggplot(validation_summary_table_total_exclude, aes(as.numeric(value),fct_inorder(as.factor(Dataset_label)))) +
geom_point(aes(shape = `Prediction`, size=`Prediction`, colour=`Prediction`), alpha=0.5) +
scale_shape_manual(values = c(R=15, NWT=18, R_excludeI=20)) +
scale_size_manual(values = c(R=3, NWT=3, R_excludeI=3)) +
scale_colour_manual(values=c(R="#bd1515", NWT="#2c15ae", R_excludeI="#339966")) +
scale_y_discrete(limits=rev)+
facet_grid(`Evaluation metric`~., scales = "free", space='free')+
labs(y="", x = "%") +
theme_bw()+
theme(legend.title=element_blank())
ggsave(width=10, height=14, file="../figs/FigX_extValidation_exclude.pdf")Figure 7 - external validation
HowdenGorrie_MIC <- readPNG("HowdenGorrie_ValidationOutput/MIC_PredRIS.png", native=TRUE)
MBIRA_MIC_Kpn <- readPNG("MBIRA_ValidationOutput/MIC_PredRIS_Kpn.png", native=TRUE)
MBIRA_MIC_Kvv <- readPNG("MBIRA_ValidationOutput/MIC_PredRIS_Kvv.png", native=TRUE)
AGAR_MIC <- readPNG("AGAR_ValidationOutput/MIC_PredRIS.png", native=TRUE)
Milan_MIC <- readPNG("MilanSanRafaelle_ValidationOutput/MIC_PredRIS.png", native=TRUE)
HailuEthiopia_DD <- readPNG("HailuEthiopia_ValidationOutput/DD_PredRIS.png", native=TRUE)
KayamaJapan_MIC <- readPNG("KayamaJapan_ValidationOutput/MIC_PredRIS.png", native=TRUE)
FeaseyMalawi_DD <- readPNG("FeaseyMalawi_ValidationOutput/DD_PredRIS.png", native=TRUE)
EUSCAPE_EURECA_MIC <- readPNG("EURECA_EUSCAPE_ValidationOutput/MIC_PredRIS.png", native=TRUE)
EgliSwitzerland_DD <- readPNG("EgliSwitzerland_2025_ValidationOutput/DD_PredRIS.png", native=TRUE)
HRYC_Spain_MIC <- readPNG("HRYC_Spain_ValidationOutput/MIC_PredRIS.png", native=TRUE)
IndiaGHRU_MIC <- readPNG("IndiaGHRU_2025_ValidationOutput/MIC_PredRIS.png", native=TRUE)
NeumannGermany_MIC <- readPNG("NeumannGermany_ValidationOutput/MIC_PredRIS.png", native=TRUE)
Whitelaw_CHAMPS_DD <- readPNG("Whitelaw_Stellenbosch_ValidationOutput/DD_PredRIS.png", native=TRUE)
Oxfordshire_Kpn_MIC <- readPNG("Oxfordshire_ValidationOutput/MIC_PredRIS_Kpn.png", native=TRUE)
Oxfordshire_Kvv_MIC <- readPNG("Oxfordshire_ValidationOutput/MIC_PredRIS_Kvv.png", native=TRUE)
IZSAM_Italy_MIC <- readPNG("IZSAM_Italy_ValidationOutput/MIC_PredRIS.png", native=TRUE)
LaPaz_Lazaro_MIC <- readPNG("IZSAM_Italy_ValidationOutput/MIC_PredRIS.png", native=TRUE)
HarrisMERINO_MIC <- readPNG("HarrisMERINO_ValidationOutput/MIC_PredRIS.png", native=TRUE)
PachecoBrazil_MIC <- readPNG("PachecoBrazil_ValidationOutput/MIC_PredRIS.png", native=TRUE)
patch <- (wrap_elements(EUSCAPE_EURECA_MIC) +
ggtitle("A) EUSCAPE & EURECA MIC (n=2133)") +
wrap_elements(KayamaJapan_MIC) +
ggtitle("B) JARBS-GNR Japan MIC (n=834)")+
wrap_elements(IndiaGHRU_MIC) +
ggtitle("C) India GHRU MIC (n=536)")+
wrap_elements(AGAR_MIC) +
ggtitle("D) AGAR GnSOP MIC (n=485)") +
wrap_elements(Oxfordshire_Kpn_MIC) +
ggtitle("E) Oxfordshire MIC (n=295)") +
wrap_elements(IZSAM_Italy_MIC) +
ggtitle("F) IZSAM MIC (n=263)")+
wrap_elements(MBIRA_MIC_Kpn) +
ggtitle("G) MBIRA MIC (n=263)")+
wrap_elements(HowdenGorrie_MIC) +
ggtitle("H) Controlling Superbugs study & \nVictorian CPE program MIC (n=212)") +
wrap_elements(Milan_MIC) +
ggtitle("I) IRCCS Ospedale San Raffaele MIC (n=198)")+
wrap_elements(HRYC_Spain_MIC) +
ggtitle("J) HURYC & CIBERINFEC MIC (n=173)")+
wrap_elements(LaPaz_Lazaro_MIC) +
ggtitle("K) HULP MIC (n=148)")+
wrap_elements(MBIRA_MIC_Kvv) +
ggtitle("L) MBIRA* MIC (n=92)")+
wrap_elements(Oxfordshire_Kvv_MIC) +
ggtitle("M) Oxfordshire* MIC (n=66)")+
wrap_elements(NeumannGermany_MIC) +
ggtitle("N) SCHARKI MIC (n=56)")+
wrap_elements(PachecoBrazil_MIC) +
ggtitle("O) Universidade Federal da \nBahia MIC (n=38)")+
wrap_elements(HarrisMERINO_MIC) +
ggtitle("P) MERINO Trial MIC (n=36)")+
wrap_elements(FeaseyMalawi_DD) +
ggtitle("Q) Queen Elizabeth Central Hospital \n(Malawi) DD (n=737)") +
wrap_elements(EgliSwitzerland_DD) +
ggtitle("R) University of Zurich DD (n=72)")+
wrap_elements(HailuEthiopia_DD) +
ggtitle("S) Addis Ababa University* \n(Ethiopia) DD (n=70)")+
wrap_elements(Whitelaw_CHAMPS_DD) +
ggtitle("T) CHAMPS DD (n=40)")
)+
plot_layout(ncol = 3)
#ggsave(height=13, width=13, file="../figs/Fig7_extValidation.png")
#ggsave(height=13, width=13, file="../figs/Fig7_extValidation.pdf")Figure S11 - external validation (supplementary - distribution vs. genotype profile and phenotype vs. genotype profile)
HowdenGorrie_dist <- readPNG("HowdenGorrie_ValidationOutput/MIC_PredGroup.png", native=TRUE)
HowdenGorrie_profile <- readPNG("HowdenGorrie_ValidationOutput/StackedBar_PredGroup_SIR.png", native=TRUE)
MBIRA_Kpn_dist <- readPNG("MBIRA_ValidationOutput/MIC_PredGroup_Kpn.png", native=TRUE)
MBIRA_Kpn_profile <- readPNG("MBIRA_ValidationOutput/StackedBar_PredGroup_SIR_Kpn.png", native=TRUE)
MBIRA_Kvv_dist <- readPNG("MBIRA_ValidationOutput/MIC_PredGroup_Kvv.png", native=TRUE)
MBIRA_Kvv_profile <- readPNG("MBIRA_ValidationOutput/StackedBar_PredGroup_SIR_Kvv.png", native=TRUE)
AGAR_dist <- readPNG("AGAR_ValidationOutput/MIC_PredGroup.png", native=TRUE)
AGAR_profile <- readPNG("AGAR_ValidationOutput/StackedBar_PredGroup_SIR.png", native=TRUE)
Milan_dist <- readPNG("MilanSanRafaelle_ValidationOutput/MIC_PredGroup.png", native=TRUE)
Milan_profile <- readPNG("MilanSanRafaelle_ValidationOutput/StackedBar_PredGroup_SIR.png", native=TRUE)
Hailu_dist <- readPNG("HailuEthiopia_ValidationOutput/DD_PredGroup.png", native=TRUE)
Hailu_profile <- readPNG("HailuEthiopia_ValidationOutput/StackedBar_PredGroup_SIR.png", native=TRUE)
Kayama_dist <- readPNG("KayamaJapan_ValidationOutput/MIC_PredGroup.png", native=TRUE)
Kayama_profile <- readPNG("KayamaJapan_ValidationOutput/StackedBar_PredGroup_SIR.png", native=TRUE)
Feasey_dist <- readPNG("FeaseyMalawi_ValidationOutput/DD_PredGroup.png", native=TRUE)
Feasey_profile <- readPNG("FeaseyMalawi_ValidationOutput/StackedBar_PredGroup_SIR.png", native=TRUE)
EUSCAPE_EURECA_dist <- readPNG("EURECA_EUSCAPE_ValidationOutput/MIC_PredGroup.png", native=TRUE)
EUSCAPE_EURECA_profile <- readPNG("EURECA_EUSCAPE_ValidationOutput/StackedBar_PredGroup_SIR.png", native=TRUE)
EgliSwitzerland_dist <- readPNG("EgliSwitzerland_2025_ValidationOutput/DD_PredGroup.png", native=TRUE)
EgliSwitzerland_profile <- readPNG("EgliSwitzerland_2025_ValidationOutput/StackedBar_PredGroup_SIR.png", native=TRUE)
HRYC_Spain_dist <- readPNG("HRYC_Spain_ValidationOutput/MIC_PredGroup.png", native=TRUE)
HRYC_Spain_profile <- readPNG("HRYC_Spain_ValidationOutput/StackedBar_PredGroup_SIR.png", native=TRUE)
IndiaGHRU_dist <- readPNG("IndiaGHRU_2025_ValidationOutput/MIC_PredGroup.png", native=TRUE)
IndiaGHRU_profile <- readPNG("IndiaGHRU_2025_ValidationOutput/StackedBar_PredGroup_SIR.png", native=TRUE)
NeumannGermany_dist <- readPNG("NeumannGermany_ValidationOutput/MIC_PredGroup.png", native=TRUE)
NeumannGermany_profile <- readPNG("NeumannGermany_ValidationOutput/StackedBar_PredGroup_SIR.png", native=TRUE)
WhitelawCHAMPS_dist <- readPNG("Whitelaw_Stellenbosch_ValidationOutput/DD_PredGroup.png", native=TRUE)
WhitelawCHAMPS_profile <- readPNG("Whitelaw_Stellenbosch_ValidationOutput/StackedBar_PredGroup_SIR_DD.png", native=TRUE)
Oxfordshire_Kpn_dist <- readPNG("Oxfordshire_ValidationOutput/MIC_PredGroup_Kpn.png", native=TRUE)
Oxfordshire_Kpn_profile <- readPNG("Oxfordshire_ValidationOutput/StackedBar_PredGroup_SIR_Kpn.png", native=TRUE)
Oxfordshire_Kvv_dist <- readPNG("Oxfordshire_ValidationOutput/MIC_PredGroup_Kvv.png", native=TRUE)
Oxfordshire_Kvv_profile <- readPNG("Oxfordshire_ValidationOutput/StackedBar_PredGroup_SIR_Kvv.png", native=TRUE)
IZSAM_Italy_dist <- readPNG("IZSAM_Italy_ValidationOutput/MIC_PredGroup.png", native=TRUE)
IZSAM_Italy_profile <- readPNG("IZSAM_Italy_ValidationOutput/StackedBar_PredGroup_SIR.png", native=TRUE)
LaPaz_Lazaro_dist <- readPNG("LaPaz_Lazaro_ValidationOutput/MIC_PredGroup.png", native=TRUE)
LaPaz_Lazaro_profile <- readPNG("LaPaz_Lazaro_ValidationOutput/StackedBar_PredGroup_SIR.png", native=TRUE)
Harris_MERINO_dist <- readPNG("HarrisMERINO_ValidationOutput/MIC_PredGroup.png", native=TRUE)
Harris_MERINO_profile <- readPNG("HarrisMERINO_ValidationOutput/StackedBar_PredGroup_SIR.png", native=TRUE)
Pacheco_Brazil_dist <- readPNG("PachecoBrazil_ValidationOutput/MIC_PredGroup.png", native=TRUE)
Pacheco_Brazil_profile <- readPNG("PachecoBrazil_ValidationOutput/StackedBar_PredGroup_SIR.png", native=TRUE)
(wrap_elements(EUSCAPE_EURECA_dist) +ggtitle("i) A (EUCAST) / \n EURECA & EUSCAPE (n=2133) - MIC distribution vs.\n genotype profile") + wrap_elements(EUSCAPE_EURECA_profile) + ggtitle("ii) A (EUCAST) / \n EURECA & EUSCAPE (n=2133) - observed phenotype vs.\n genotype profile"))/
(wrap_elements(IndiaGHRU_dist) +ggtitle("iii) B (EUCAST) / \nIndia GHRU (n=536) - MIC distribution vs.\n genotype profile") + wrap_elements(IndiaGHRU_profile) + ggtitle("iv) B (EUCAST) / \nIndia GHRU (n=536) - observed phenotype vs.\n genotype profile")) 
ggsave(height=13, width=11, file="../figs/FigS11_extValidation_genoprofiles_1.png")
ggsave(height=13, width=11, file="../figs/FigS11_extValidation_genoprofiles_1.pdf")
(wrap_elements(AGAR_dist) +ggtitle("v) C (EUCAST) / \n AGAR GnSOP (n=485) - MIC distribution vs.\n genotype profile") + wrap_elements(AGAR_profile) + ggtitle("vi) C (EUCAST) / \n AGAR GnSOP (n=485) - observed phenotype vs.\n genotype profile")) /
(wrap_elements(Oxfordshire_Kpn_dist) +ggtitle("vii) D (EUCAST) / \n Oxfordshire (n=295) - MIC distribution vs.\n genotype profile") + wrap_elements(Oxfordshire_Kpn_profile) + ggtitle("viii) D (EUCAST) / \n Oxfordshire (n=295) - observed phenotype vs.\n genotype profile")) 
ggsave(height=13, width=11, file="../figs/FigS11_extValidation_genoprofiles_2.png")
ggsave(height=13, width=11, file="../figs/FigS11_extValidation_genoprofiles_2.pdf")
(wrap_elements(HRYC_Spain_dist) +ggtitle("ix) E (EUCAST) / \n HURYC & CIBERINFEC (n=173) - MIC distribution vs.\n genotype profile") + wrap_elements(HRYC_Spain_profile) + ggtitle("x) E (EUCAST) \ \n HURYC & CIBERINFEC (n=173) \n - observed phenotype vs. genotype profile")) /
(wrap_elements(LaPaz_Lazaro_dist) +ggtitle("xi) F (EUCAST) / \n HULP (n=148) - MIC distribution vs.\n genotype profile") + wrap_elements(LaPaz_Lazaro_profile) + ggtitle("xii) F (EUCAST) \ \n HULP (n=148) \n - observed phenotype vs. genotype profile")) 
ggsave(height=13, width=11, file="../figs/FigS11_extValidation_genoprofiles_3.png")
ggsave(height=13, width=11, file="../figs/FigS11_extValidation_genoprofiles_3.pdf")
(wrap_elements(Kayama_dist) +ggtitle("xiii) G (CLSI) / \n JARBS-GNR Japan (n=834) - MIC distribution vs.\n genotype profile") + wrap_elements(Kayama_profile) + ggtitle("xiv) G (CLSI) / \n JARBS-GNR Japan (n=834) - observed phenotype vs.\n genotype profile")) /
(wrap_elements(MBIRA_Kpn_dist) +ggtitle("xv) H (CLSI) / \n MBIRA (n=263) - MIC distribution vs.\n genotype profile") + wrap_elements(MBIRA_Kpn_profile) + ggtitle("xvi) H (CLSI) / \n MBIRA (n=263) - observed phenotype vs.\n genotype profile")) 
ggsave(height=13, width=11, file="../figs/FigS11_extValidation_genoprofiles_4.png")
ggsave(height=13, width=11, file="../figs/FigS11_extValidation_genoprofiles_4.pdf")
(wrap_elements(HowdenGorrie_dist) +ggtitle("xvii) I (CLSI) / \n Controlling Superbugs study & \nVictorian CPE program (n=212) - MIC distribution vs.\n genotype profile") + wrap_elements(HowdenGorrie_profile) + ggtitle("xviii) I (CLSI) / \n Controlling Superbugs study & \nVictorian CPE program (n=212) - observed phenotype vs.\n genotype profile")) /
(wrap_elements(Feasey_dist) +ggtitle("xix) J* (EUCAST/BSAC) / \n Queen Elizabeth Central Hospital Malawi (n=737) \n - DD distribution vs. genotype profile") + wrap_elements(Feasey_profile) + ggtitle("xx) J* (EUCAST/BSAC) / \n Queen Elizabeth Central Hospital Malawi (n=737) \n - observed phenotype vs. genotype profile")) 
ggsave(height=13, width=11, file="../figs/FigS11_extValidation_genoprofiles_5.png")
ggsave(height=13, width=11, file="../figs/FigS11_extValidation_genoprofiles_5.pdf")
(wrap_elements(Hailu_dist) +ggtitle("xxi) K* (Kvv, CLSI) / \n Addis Ababa University* (n=70) .\n - DD distribution vs. genotype profile") + wrap_elements(Hailu_profile) + ggtitle("xxii) K* (Kvv, CLSI) / \n Addis Ababa University* (n=70) \n - observed phenotype vs. genotype profile")) /
(wrap_elements(EgliSwitzerland_dist) +ggtitle("xxiii) L* (EUCAST) / \n University of Zurich (n=72) - \n DD distribution vs. genotype profile") + wrap_elements(EgliSwitzerland_profile) + ggtitle("xxiv) L* (EUCAST) / \n University of Zurich (n=72) \n - observed phenotype vs. genotype profile")) 
ggsave(height=13, width=10, file="../figs/FigS11_extValidation_genoprofiles_6.png")
ggsave(height=13, width=10, file="../figs/FigS11_extValidation_genoprofiles_6.pdf")
(wrap_elements(WhitelawCHAMPS_dist) +ggtitle("xxv) M* (CLSI) / \n CHAMPS (n=40) - DD distribution vs.\n genotype profile") + wrap_elements(WhitelawCHAMPS_profile) + ggtitle("xxvi) M* (CLSI) / \n CHAMPS (n=40) - observed phenotype vs.\n genotype profile")) /
(wrap_elements(IZSAM_Italy_dist) +ggtitle("xxvii) IZSAM (n=283) - MIC distribution vs.\n genotype profile") + wrap_elements(IZSAM_Italy_profile) + ggtitle("xxviii) IZSAM (n=283) - observed phenotype vs.\n genotype profile")) 
ggsave(height=13, width=11, file="../figs/FigS11_extValidation_genoprofiles_7.png")
ggsave(height=13, width=11, file="../figs/FigS11_extValidation_genoprofiles_7.pdf")
(wrap_elements(Milan_dist) +ggtitle("xxix) IRCCS Ospedale San Raffaele (n=198) -\n MIC distribution vs. genotype profile") + wrap_elements(Milan_profile) + ggtitle("xxx) IRCCS Ospedale San Raffaele (n=198) \n - observed phenotype vs. genotype profile")) /
(wrap_elements(MBIRA_Kvv_dist) +ggtitle("xxxi) MBIRA (Kvv, CLSI) (n=92) - MIC distribution vs.\n genotype profile") + wrap_elements(MBIRA_Kvv_profile) + ggtitle("xxxii) MBIRA (Kvv, CLSI) (n=92) - observed phenotype vs.\n genotype profile")) 
ggsave(height=13, width=10, file="../figs/FigS11_extValidation_genoprofiles_8.png")
ggsave(height=13, width=10, file="../figs/FigS11_extValidation_genoprofiles_8.pdf")
(wrap_elements(Oxfordshire_Kvv_dist) +ggtitle("xxxiii) Oxfordshire (Kvv, EUCAST) (n=66) \n - MIC distribution vs. genotype profile") + wrap_elements(Oxfordshire_Kvv_profile) + ggtitle("xxxiv) Oxfordshire (Kvv, EUCAST) (n=66) \n - observed phenotype vs. genotype profile")) /
(wrap_elements(NeumannGermany_dist) +ggtitle("xxxv) SCHARKI (n=56) - MIC distribution vs.\n genotype profile") + wrap_elements(NeumannGermany_profile) + ggtitle("xxxvi) SCHARKI (n=56) - observed phenotype vs.\n genotype profile")) 
ggsave(height=13, width=10, file="../figs/FigS11_extValidation_genoprofiles_9.png")
ggsave(height=13, width=10, file="../figs/FigS11_extValidation_genoprofiles_9.pdf")
(wrap_elements(Feasey_dist) +ggtitle("xxxvii) Universidade Federal da Bahia (n=38) \n - MIC distribution vs. genotype profile") + wrap_elements(Feasey_profile) + ggtitle("xxxviii) Universidade Federal da Bahia (n=38) \n - observed phenotype vs. genotype profile")) /
(wrap_elements(EgliSwitzerland_dist) +ggtitle("xxxix) MERINO trial (n=36) \n - DD distribution vs. genotype profile") + wrap_elements(EgliSwitzerland_profile) + ggtitle("xl) MERINO trial (n=36) \n - observed phenotype vs. genotype profile")) 
numbers for summary
validation_phenotypes <- read_delim("../tables/TableS5_phenotype_metadata_validation.tsv")
validation_genotypes <- read_delim("../tables/TableS4_genotypes_validation.tsv")
validation_phenotypes_summary_table <- validation_phenotypes %>% group_by(Dataset, Dataset_alias, `Assembly method`, Laboratory.Typing.Method, Testing.standard, Laboratory.Typing.Platform) %>% count()
#Create foundation for the table
#write_tsv(validation_phenotypes_summary_table, "../tables/TableS3_validation_summary.tsv", na="-")